CHAPTER 16 MICRO ANALYSIS OF OFFICE MARKETS INTRODUCTION ANALYZING OFFICE LOCATIONS But do Office Rents Fully Reflect Location Amenity Differentials? ANALYZING OFFICE PROJECTS: ESTIMATING ACHIEVABLE RENTS AND ABSORPTION SCHEDULES COLLECTION OF DATA FOR COMPETING PROPERTIES Identify Comparable Office Properties Collect Information on the Identified Comparables DATA ANALYSIS Using the Competitive Differentials Technique to Assess Project Strength and Achievable Rental Rates Critique Using Hedonic Methodologies to Estimate Achievable Lease Rates Critique Developing Office Project Absorption and Revenue Schedules CHAPTER SUMMARY QUESTIONS REFERENCES AND ADDITIONAL READINGS APPENDIX 16A: SOME EMPIRICAL RESULTS ON OFFICE FIRM LOCATION - 432 -
INTRODUCTION Once the macroeconomic analysis of the office market under consideration is completed and a preliminary development scenario is formed, microeconomic analysis needs to be conducted to help refine the design of the proposed office project, assess its competitive position, and evaluate its revenue-generating ability. As it is the case for other property types, the major components of microeconomic analysis of office markets includes: 1) site and location analysis, and 2) project marketability analysis. The underlying premise of office location analysis is that location is a major determinant of an office project s desirability and income-generating ability and, therefore, of its success. Matching the location and space needs of potential office tenants to site and project characteristics always enhances an office project s competitive strength. Marketability analysis is necessary in order to assess the project s expected rental revenue stream and absorption schedule. ANALYZING OFFICE LOCATIONS In analyzing office locations, it is important to bear in mind that the strength and nature of location influences exerted by site-specific factors may vary across the different types of office firms. Although, in general, office firms are not thought to be highly heterogeneous in terms of their space and location requirements, they may be differentiated in terms of: General type--general purpose office firms vs. medical offices or law offices Type of operations performed-- front or back office functions Type of labor used executive vs. clerical etc. Firm size Below we briefly discuss the factors that are relevant in office location analysis and indicate whether some types of firms may have stronger or weaker preferences for certain location attributes. These factors are presented in Box 16.1 and include both location amenities and location constraints. Location amenities include productive amenities, worker/non-productive amenities, prestige, and visibility, while location constraints refer primarily to institutional constraints on commercial development. Productive or firm amenities refer to locational attributes the can help a firm increase its productivity and/or reduce its costs. Such locational attributes include close-by access to basic business support services, broader access to business services agglomerations, access to white-collar labor, access to airports and freeways, and jurisdictional property tax rates. Close-by access to basic business support activities, such as banking, restaurants, and shopping, provides for a more conducive working environment that can reduce the time cost of reaching basic services typically sought by employees during working hours. - 433 -
BOX 16.1 WHAT LOCATION ATTRIBUTES MAY MATTER? (See attached empirical results in the Appendix 16.A) Location Amenities Office Firm/Production Amenities (all contribute toward lower business costs) Close-by access to basic business support activities Close-by access to facilities that can provide basic services typically sought by employees during working hours, such as banking, restaurants, and shopping can help reduce the time costs of accommodating such needs Access to business service agglomerations Traditionally thought to reduce the cost of interpersonal contacts and, possibly, labor search costs, but the effect of this factor is weakening due to advances in information/telecommunication technologies Access to white-collar labor Thought to contribute toward lower labor costs; central locations may be sought if specialized labor is dispersed, while suburban locations may be sought if labor skills are clustered Airport and freeway access Facilitates business trips; airport access becomes increasingly important Property tax rates Property tax rates matter only when the spatial demand for office sites is inelastic because they are passed on to office lease rates Office Worker/Residential Amenities (contribute toward lower labor costs) Access to amenable residential communities with: High quality educational system Low crime rates High levels of public services for given levels of property taxes Access to shopping, recreational amenities (entertainment, ocean) Prestige and Visibility Location Constraints (increase business rents, if binding) Development constraints: zoning and density controls, growth moratoria Broader access to business services agglomerations matters because it may reduce the cost of interpersonal contacts and, possibly, labor search costs. The more specialized the office-using activity is the greater the need for central control functions and access to business services agglomerations. Such location attribute appears to have little relevance for back office functions. Empirical evidence shows that the importance of access to business service centers may have been weakening, potentially - 434 -
due to advances in information and telecommunications technologies (Sivitanidou, 1997). Some more specialized firms, such as law or medical companies, tend to cluster around certain facilities that are highly associated with their business activities, such as, courthouses and hospitals, respectively. Access to white-collar labor can help office firms decrease labor costs, as it may reduce their search costs and motivate workers living close by to accept lower wages in exchange of the lower commuting costs they will enjoy. Central locations, such as downtown, may be sought if labor skills are dispersed within the urban area or if the firm is using a large and highly heterogeneous labor force. Suburban locations may be sought if labor skills are clustered. Airport and freeway access is sought by office firms since it facilitates business trips. Airport access is becoming increasingly important, especially for corporate divisions, due to the need for more frequent business trips. Sivitanidou (1996) presents evidence that strongly supports the proposition that proximity to airports matters in office location decisions. Proximity to freeways and freeway junctions may be also valued by firms as it provides access to other firms, labor, and potential customers over a larger geographic area. Property tax rates matter only when spatial demand for office sites is inelastic. All else equal, higher property tax rates should discourage development, thereby inducing an upward shift in the office property supply curve. Such an upward shift will result in significantly higher rents only if office space demand is inelastic, in which case the property tax rate differentials will most likely be passed on to the office tenant. In this sense, jurisdictional property tax rate differentials may affect firm production costs through their potential effect on rental rates. Worker/residential amenities, such as, access to amenable residential areas and communities with high quality education, low crime rates, high quality of public services for a given level of taxes, and convenient access to shopping and recreational destinations, matter in office location analysis because they may indirectly help reduce a firm s cost. In particular, good access to amenable residential areas can facilitate recruitment of skilled labor and, thereby, reduce labor search costs. Furthermore, there is considerable empirical evidence by skill that happier workers are willing to accept lower wages. Such residential amenities become more important the higher the proportion of a firm s managerial labor. Prestige and visibility are also important from the demand side, especially for high profile firms and company headquarters. Usually, downtown locations or prestigious suburban nodes are the preferred locations for these types of firms. Location constraints refer to institutional constraints imposed by local governments on commercial development, such as zoning, density controls, and growth moratoria. If such constraints are binding, that is, if they restrict the supply of office space to levels below the ones dictated by market demand, they can lead to supply shortages and, thereby, to higher office space rents. Understanding the degree to which the project s node or submarket is constrained can provide useful insights as to whether the site s locational advantages are fully capitalized into office rent differentials compared to office buildings located in other competing nodes. We elaborate on this issue in the next section. - 435 -
But, Do Office Rents Fully Reflect Location Amenity Differentials? The location component of office rents is the result of the interplay between location amenities and location constraints. An issue that is at the heart of office location analysis is the extent to which office rents capitalize location-amenity differentials within contemporary metropolitan office markets (see Box 16.2). This issue is very important because the conventional view is that amenity differentials across office locations are fully capitalized on office rents. This view is the underlying premise of the competitive differentials technique often used to derive achievable rents for a new office building at a given location. Theoretical analysis presented by Sivitanidou and Wheaton (1992) suggests that location-amenity differentials may be fully reflected in office rent differentials only if the land market is constrained. In unconstrained markets, most of the location-amenity differentials should be reflected in wages. To understand the argument of rent and wage capitalization consider for the sake of simplicity a city with two business centers or office submarkets, that are the same in terms of size, office space rents, and worker wages. Now let s introduce an exogenous (not related to rents or wages) productive advantage in Center 1 of, lets say, $1000 per worker per year. This simply means that it is cheaper to do business at Center 1 by that amount. As the city grows, new firms will seek space in Center 1, because of its productive advantages. As a result, Center 1 will grow bigger in terms of both employment and commercial floor space. As Center 1 grows in size the average commuting cost of its workers increases too necessitating higher wages. Center 1 will continue to expand until its wages and rents increase enough to erode its productive advantage. Thus, at the new equilibrium, the rent and wage differentials between Center 1 and Center 2 should be such that they compensate for the exogenous production cost advantage of $1,000 per worker, as in (16.1): a (R 1 R 2 ) + (W 1 W 2 ) = $1,000/worker ; R 1 >R 2 and W 1 >W 2 (16.1) where a : square feet per worker R 1, R 2 : office space rent per square foot in Center 1 and Center 2, respectively W 1, W 2 : wage per worker in Center 1 and Center 2, respectively The question that arises is what percent of the production cost differential will be reflected in higher rents and what percent in higher wages. Should the analyst care? The answer is yes, because if rents do not fully or nearly fully capitalize production cost differences, the competitive differential technique may not be appropriate for estimating the rent of the subject property when comparables from different submarkets are included in the analysis. Notice, however, that the validity of this technique is questioned only as far as locational amenities are concerned. 94 94 As the reader must know by now application of the competitive differential technique involves and a host of other attributes, such as structure and project quality and amenities. - 436 -
BOX 16.2 BUT, DO OFFICE RENTS REFLECT LOCATIONAL AMENITIES 95 (i.e. Production or Tenant Cost Differentials)? The location component of office rents is the result of the interplay between location amenities (production cost advantages) and location constraints (zoning, density controls, development moratoria). The question is whether office rents fully reflect location amenities under alternative land market conditions. Constrained (Regulated) Markets Only in constrained land markets office rents may almost fully capitalize the value of location productivity differentials Unconstrained (Non-Regulated) Markets In competitive markets, it is wages, not rents that capitalize the major portion of location productivity differentials Implications for Micro-Analysis The competitive differential technique may be flawed when applied to competitive office markets for it assumes that differences in office rents across locations must fully reflect amenity differentials. As Equation (16.1) suggests, only if the wage differential between the two employment nodes is zero (W 1 W 2 =0) rent differentials would fully capitalize the $1000/worker productivity advantage of Center 1. Theoretical analysis shows that the more constrained (regulated) a market is the greater the percentage of the productive advantage that is capitalized on rents as opposed to wages. The reason is that the more Center 1 is constrained to expand due to development controls, the smaller its wage differential from Center 2. On the contrary, if the market is not regulated in a way that restrains office space supply and the growth of Center 1, office rent differentials between the two centers will be very small and most of the productive advantage will be capitalized on wages [for a more elaborate presentation of this analysis see Sivitanidou and Wheaton (1992)]. These findings have not yet been established empirically, but if valid, they do have some important practical implications regarding the use of the competitive differential 95 See Brennan, T., R. Cannaday, and P. Colwell. 1984. Office Rent in the Chicago CBD. AREUEA Journal 12: 243-260; Sivitanidou, R. and W. Wheaton. 1992. Wage and Rent Capitalization in the Commercial Real Estate Market. Journal of Urban Economics 31: 206-229; Sivitanidou, R. 1996. Do Office Firms Value Access to Service Employment Centers? A Hedonic Value Analysis within Polycentric Los Angeles. Journal of Urban Economics 40: 1-27. - 437 -
technique for the estimation of the rent of a planned office project. More specifically, the findings suggest that such applications may be seriously inaccurate when the area s land market is not constrained and the sample of competing properties includes office buildings located in different submarkets/nodes with considerably different location attributes. In such a case, the analyst needs to examine carefully whether variations in specific location attributes translate consistently to proportionate variations in lease rates across properties. This is a very difficult task given the multitude of attributes that may differ across comparables and underscores the need for hedonic regression techniques in assessing the true effect, if any, of location-amenity differentials on office space rents. In the case of highly regulated land markets, where office space supply is constrained, the competitive differential technique may better measure relative office rent and price differentials across competing locations, although hedonic techniques would still more accurately capture the effect of the different location attributes on rents and prices. ANALYZING OFFICE PROJECTS: ESTIMATING ACHIEVABLE RENTS AND ABSORPTION SHCEDULES Once the location-analysis phase is completed and the development scenario has been refined to match the space needs of the tenants most likely to be attracted at a given site, the analyst needs to proceed with the evaluation of the competition. This analysis will help assess the prospects for further enhancing the project s competitive strength and derive the bottom-line figures required for assessing its economic feasibility. These figures include: Office rent schedule Absorption schedule, and Revenue schedule Assessment of the project s achievable rental rates requires analysis of the competition and quantification of how the different office structure and location attributes translate into rental rate differentials within the local marketplace. Such an assessment will help the analyst not only estimate the most likely achievable rent for the planned office structure at the specific location, but also evaluate whether modifying certain structure or project attributes will help maximize achievable rates. Furthermore, the estimation of achievable rates can help determine the optimal office development density for the specific site considered. It is important to emphasize that when analyzing office rents the unit of analysis is not the structure per se, but lease transactions. Thus, for representative samples of lease transactions, the data that need to be collected include not only the attributes of the space, building, and location associated with the lease contracts, but also a whole array of lease characteristics that may have an effect on the effective rent paid by the tenant. Assessment of the project s absorption schedule, in combination with the estimates of achievable rental rates, can help estimate the property s expected revenue schedule, and therefore, its feasibility from the market perspective. Furthermore, evaluation of the project s absorption schedule under alternative entry scenarios can help make prudent - 438 -
decisions regarding the most appropriate/profitable time of market entry, as well as project phasing for projects involving more than one building. In estimating the expected revenue for a planned office development, the analyst needs to make sure that a reasonable lease expiration and rollover schedule, as well as rent escalation provisions, are assumed and properly accounted in the calculation. Special caution needs to be exercised when assessing the absorption schedule for space expected to be vacated by expiring leases in the future. Given the above discussion, the estimation of an office project s most likely rental rate and absorption schedule involves the following broader steps: 1) Data collection Identification of comparable properties Collection of information on lease transactions, lease characteristics, and other attributes associated with the comparable properties 2) Data analysis Application of simple accounting techniques, and specifically the competitive differential technique, or Econometric techniques, and specifically, hedonic methodologies These steps are discussed in detail in the following sections. 1. COLLECTION OF DATA FOR COMPETING OFFICE PROPERTIES Identify Comparable Office Properties The first step in the collection of data for competitive analysis is the identification of comparable office properties through brokerage firms, multiple listing services, and/or windshield surveys. The focus of the data collection effort is somewhat different depending on whether the competitive differential or the econometric technique is used: When applying the competitive differential technique the analysis typically focuses on comparable office properties (existing, under construction and planned) within the submarket the subject property is located in the case of larger markets. Less detailed surveys of other competing office space clusters in the metropolitan office market are also typical in evaluating the competition. When applying hedonic analysis techniques it is not necessary to restrict the sample of office properties only within the submarket of the subject site; actually, it is preferable that the sample includes properties with significant differences in location and structure attributes. Thus, the sample may be expanded to include properties that may not be strictly competitive with the project under consideration. The analyst may be better off by applying both techniques, if time, budget, and data availability allow it. Although the econometric technique is likely to produce more accurate estimates of the project s achievable rental rate, estimates using the competitive differential - 439 -
technique can provide an alternative estimate, against which the analyst can evaluate the reasonableness of the result of the former methodology. Collect Information on the Identified Comparables Once the comparable office properties and their locations have been identified, the analyst needs to compile information on lease rates, lease characteristics, property, and location attributes from local rental agents, brokers, and appraisers, and/or through field surveys. Box 16.3 presents a (not necessarily exhaustive) list of the data that should be collected. Some comments are warranted here regarding the relevance of the factors listed. Lease Rates and Lease Characteristics Lease rates are expressed in dollars per square foot per year. In trying to understand the supply-demand dynamics of office markets and how they affect market rates, the important rent measure is the effective rent, which is different from the asking rent and the contract or quoted rent. The effective rent is the actual rent paid by the tenant after taking into account concessions, such as free rent, excessive tenant improvement allowances, expense caps and free parking, stipulated by the lease agreement. In general, the larger the lease the more difficult is to determine the actual effective rent paid by the tenant (see Peiser and Schwanke, 1992). Thus, it is important that the analyst collects information not only on base-year lease rates, but also on an array of lease characteristics discussed below. The date of lease transaction is needed in order to control for the effect of market conditions on lease rates at the time the lease was signed The base year or contract lease rate is the basic information, which, in combination with information on other lease terms, can help develop a measure of the overall effective rent implied by the lease agreement The type of lease rate is very important in developing a consistent measure of effective market rent across lease transactions since it clarifies whether the quoted contract rate is net of taxes (N), net of taxes and utilities (NN), or net of taxes, utilities, and operating expenses (NNN). The length of the lease may influence contract rates through several avenues. For example, if expectations for improving market conditions are prevalent at the time the transaction takes place, then longer leases may be associated with higher contract rates. Independently of expectations about future market conditions, however, a longer lease reduces the risk or volatility of the landlord s cash flows. As a result, the landlord may be willing to accept lower rates for longer-term commitments by prospective tenants, especially given the high turnover costs (tenant improvements) typically associated with new tenants in office buildings. On the other hand, by accepting a long-term lease, the landlord is forgoing the ability to take advantage of potential market rent increases above those stipulated by any CPI escalation clauses included in the contract. Thus, the strength of the market at the time the lease is signed may be the deterministic factor of the relationship (positive or negative) between the length of the lease the contract rate. Data presented by DiPasquale and Wheaton (1996) suggest an average length lease of 5 years with 3-year and 10-year leases having also relatively high frequencies. - 440 -
BOX 16.3 ANALYZING OFFICE RENTS/LEASE RATES: ANALYSIS COMPONENTS Data sources Brokerage firms Multiple listing services Windshield surveys Note: Data for strictly competing properties should be collected if the competitive differential technique is applied. There are no such restrictions if the hedonic methodology is used, as this technique can control for a wide range of property and location attributes. Required Micro Data Lease Rates and Lease Characteristics Date of the Lease Transaction Base Year Lease Rates Lease Terms Type of lease rate: Gross, N, NN, NNN Length of the lease Size of the lease Escalation clauses Stop and other clauses Concessions (free rent, etc.) Tenant Improvements (TIs) Space Attributes Quality and type of space (flexible design or not) Location within structure (floor, corner office or not, etc.) Structure Attributes Quality of lobbies and elevators Structure amenities (such as restaurants, gyms, etc.) Parking availability Building access (easiness of access to the building) Location Attributes (see Box 16.1) Note: See Brennan, T., R. Cannaday, and P. Colwell. (1984). Office Rent in the Chicago CBD. AREUEA Journal 12: 243-260. The size of the lease, or in other terms, the amount of square footage committed in the lease transaction, may be negatively associated with office rents, as landlords may be willing to offer volume discounts when leasing larger blocks of space. Rent escalation clauses provide for a formula for adjusting rents on the basis of widely used economic indicators, usually the CPI; in soft markets, rent escalation is less - 441 -
steep in that rents are allowed to adjust every two years or in the middle of the lease term. In the presence of escalation clauses, contract rates should be lower compared to leases with no escalation clauses. Stop clauses represent a ceiling amount for operating expenses (the dollar stop), usually determined on the basis of estimated expenses during the first year of the lease. Any expenses above that amount are paid by the tenant. Therefore, the stop amount should be positively associated with the base lease rate, since the higher the amount the higher the burden on the landlord. Concessions, including primarily free rent, constitute another important piece of information in developing an accurate and consistent measure of effective market rent through time. It appears that when the market is soft, landlords prefer to provide rent concessions and maintain a higher contract rate rather than directly lower the contract rates. As a result, concessions are more prevalent during periods of excess supply and high vacancy rates. Tenant improvements, representing tenant finish allowances for the interior space (including, ceilings, walls, flooring, and telephone and electrical outlets), and commonly referred to as TIs, are in substance an additional form of concessions. Landlords always provide some form of tenant improvement allowances, but it has been observed that during periods of serious excess supply such allowances are considerably higher and are presumably provided as an additional incentive to lure tenants. Again, information on tenant improvements can help further refine the effective rent calculation across leases and through time. Space Attributes The second set of factors that needs to be examined when analyzing lease rates in comparable office buildings includes the characteristics of the specific space the lease contract refers to. Space attributes, such as the floor on which the leased space is located or its position within the structure (corner offices) may also contribute to variations in lease rates. Structure Attributes The attributes of the structure within which the space is located, such as construction quality, quality of the lobbies and elevators, structure amenities (restaurants, shops, gyms, etc.), parking availability, easiness of access to the building, etc., represent another important set of factors that need to be taken into account when examining differences in lease rates across comparable properties. Location Attributes The final set of data that needs to be collected includes the location attributes associated with the comparable properties for which lease transaction data are available. These have been discussed in detail in the previous section. 2. DATA ANALYSIS Once the information has been collected and systematically tabulated the analyst can apply the competitive differential technique or hedonic methodologies in order to evaluate the income-earning ability of the project and assess the possibilities for enhancing its competitive position. In particular, application of these techniques will help the analyst to: Quantify the competitive strength of the project by estimating its Competitive Position Index (CPI) Identify the sets of attributes that contribute the most to project value, thereby setting the stage for refining project design and maximizing effective lease rates - 442 -
Estimate achievable lease rates given project and location attributes Using the Competitive Differential Technique to Assess Project Strength and Achievable Rental Rates The premise of the competitive differential technique, as it applies to the office market, is that differences in office lease rates across competing office properties reflect differences in lease characteristics, structure/project attributes and location amenities. In order to review the application of this technique in the office market we will use Clapp s (1987) example of the competitive spreadsheet. In reviewing this example, we will elaborate on how it can be used to estimate project rent and absorption rate. For the sake of simplicity, Table 16.1 presents an abbreviated version of this spreadsheet that includes information only for the subject and two other comparable properties. Notice that a base rent figure for the subject property is included only for illustration purposes. When dealing with a new project the base rent typically is not known and this analysis is used as a means for estimating it. 96 Thus, in the analysis steps described below it is assumed that the subject s base rent is not known (for a more detailed discussion of the calculations involved in applying the competitive differential technique see Chapter 9). (1) Construct a competitive spreadsheet where the columns represent competing office properties and the rows important lease, structure, and location attributes that may influence a property s lease rate (2) Assign weights to each attribute based on its contribution to project value or rental rate, using experience and judgment; interviewing local brokers and firms occupying competitive space regarding the importance of each attribute can help more accurately determine these weights (3) Develop scores/indices for each of the attributes of the subject property and each of the competitive properties (4) Estimate total unweighted and/or weighted amenity indices for each of the comparable properties (5) Estimate adjusted unweighted (weighted) amenity indices, as a property s total unweighted (weighted) amenity index minus the unweighted (weighted) rent score/index, respectively. For example, the adjusted weighted amenity index for comparable 1, AWAI 1, is calculated as in (16.1), where WAI 1 is the weighted amenity index for comparable 1: AWAI 1 = WAI 1 Weighted Rent Score = 5,798 400 =5398 (16.2) 96 Market rent information for an office building that is still in the pipeline may be available only if some space has been pre-leased. - 443 -
Table 16.1 An Example of the Simple Competitive Differential Technique (From Clapp, J. 1987. Handbook for Real Estate Market Analysis. Englewood Cliffs, NJ: Prentice-Hall) Structure of the Competitive Spreadsheet Subject C 1... C n Weights LEASE/SALES PROVISIONS Base Rent $14.00 $18.00 $12.00 4 Escalation 75.00% 50.00% 100.00% 4 Stop Clause $1.50 $1.00 $1.25 2 ACCESS Homes of Executives (min) 5 3 10 3 Homes of Clerical Labor (min) 8 11 6 3 Downtown Offices (min) 12 10 8 4 NEIGHBORHOOD AMENITIES Same across properties BUILDING/SITE CHARACTERISTICS Parking Spaces per 1,000 sq. ft. 3.33 2.86 2.5 4 Parking Cost per Space $30.00 $25.00 $35.00 4 Parking Spaces within 15 min. 450 800 200 4 Development of Amenity Indices Subject C 1... C n Weights LEASE/SALES PROVISIONS Base Rent 100 150 4 Escalation 133 200 100 4 Stop Clause 150 100 125 2 ACCESS Homes of Executives 200 333 100 3 Homes of Clerical Labor 138 100 183 3 Downtown Offices 100 120 150 4 NEIGHBORHOOD AMENITIES BUILDING/SITE CHARACTERISTICS Parking Spaces 133 114 100 4 Parking Cost 117 140 100 4 Parking Spaces within 15 min 225 400 100 4 TOTAL UNWEIGHTED AMENITY INDEX 1,196 1,608 1,108 TOTAL WEIGHTED AMENITY INDEX 4,146 5,798 3,900 Subject Property Base Rent Estimate Subject C 1 C n Amenity Index 4,146 5,798 3,900 Less : Base rent 516 400 600 Equals: Adjusted Index 4,146 5,398 3,300 Subject Amenity Index/ Comparable's Index 0.76806 1.25615 Rent Estimates $13.82 $15.07-444 -
(6) Estimate the base rent, R S, for the subject property relative to each comparable using the subject s total amenity index, TAI S, and each comparable s adjusted amenity index, AAI i, as follows: TAIS R S = ATAI i R i (16.3) where TAI S : (weighted or unweighted) total amenity index for subject ATAI i : adjusted (weighted or unweighted) total amenity index for comparable i R i : base rent for comparable i Note that the subject property s total amenity index (TAI S ) does not need to be adjusted since the rent of a planned project is not known and, therefore, normally a score for this variable will not be available to the analyst to be added to the total amenity score. In case that the subject is an existing property, a rent figure will most likely be available. If the analyst wants to simply evaluate whether this rent figure is consistent with the property s competitive position, then formula (16.3) provides a means for making such an evaluation. In such a case, the subject property s total amenity index may need to be adjusted too, depending on whether the rent score was taken into account when it was calculated. Continuing with the example presented in Table 16.1, we applied formula (16.3) using comparable 1 s rent as basis, the adjusted weighted amenity index for comparable 1, and the subject property s weighted amenity index. Hence, R S was estimated as: R S = (4,146/5,398) $18 = 0.76806 $18 = $13.82 Obviously, using formula (16.3) the analyst can derive as many alternative base-rent estimates for the subject property as the comparables for which rent information is available. These rent estimates will, in all probability, be different. The analyst can use the average of these alternative estimates as the project s base rent if a single-point estimate is sought. In computing this average, the analyst may want to use larger weights for rent estimates associated with properties considered to better proxy the subject s performance. Critique The results of applications of the competitive differential technique for the estimation of achievable office project rents should be viewed with skepticism. First, the methodology assumes that differences in location attributes across office properties are fully reflected in their rent differentials. As pointed out earlier, theoretical analysis suggests that this assumption may not be valid if the land market is not constrained in a binding way. Second - 445 -
and most importantly, the technique entails serious difficulties with objectively and accurately assigning weights to the different lease, structure, and location attributes that affect office lease rates in a way that is consistent both across attributes and across properties. Moreover, the validity of the assigned weights can not be confirmed without using econometric techniques. In sum, competitive differential techniques should be used with great caution in the estimation of an office property s achievable rents, but can provide an alternative estimate that can be used to test the reasonableness of estimates derived through hedonic valuation techniques. Using Hedonic Methodologies to Estimate Achievable Office Lease Rates Hedonic valuation techniques should in theory provide more accurate estimates of the rent an office project will command given its structure and location attributes. Actually, application of hedonic methodologies using a sample of lease transactions can allow the analyst to estimate the rate that a particular amount of square footage on a particular floor of the building under consideration will command, if the size of lease and floor on which the space is located are included as independent variables in the estimated regression model. Since this technique has been reviewed rather extensively in Chapter 9, this discussion will focus only on: - the set of critical variables that need to be incorporated in the case of hedonic office rent formulations - the usefulness of the technique in office project assessment Variables in Hedonic Office-Rent Models In estimating hedonic office rent models, nonlinear functional forms should be used for the reasons discussed in the residential section. Such nonlinear hedonic lease rate models need to account for the whole range of attributes that may affect lease rates in a given office structure. As discussed in the competitive differential technique these include: lease terms space and structure attributes location attributes Lease Rate =f (X lease terms, X structure traits, X location traits ) (16.4) Table 16.2, reprinted from Clapp (1987), although not complete, provides an example of how the data used for estimating hedonic office rent equations may look like. Potentially important variables omitted from Table 16.2 include the time each lease was signed (if the sample includes leases signed in different years), other productive amenities besides access, worker amenities, other lease terms, and institutional supply restrictions (if they vary across the locations with which the leases are associated with). One of the variables included in this - 446 -
table is the vacancy rate, but its inclusion in the hedonic rent formulation is highly questionable from an econometric point of view. 97 The important difference of hedonic office models from the residential ones is the inclusion of lease terms, as well as some location attributes that may not be as relevant in residential rent analysis, such as access to other firms, access to labor, airports, and, especially, jurisdictional and other institutional supply constraints on commercial development at the different nodes. As indicated earlier, the latter may be very relevant in terms of correctly quantifying the capitalization of amenity differentials across office locations on office lease rates. If the lease transactions included in the sample are associated with properties located in different office nodes/submarkets within the metropolitan area, it is typical to add in the specification dummy variables indicating the node/submarket within which each property is located. Furthermore, if lease transactions for different time periods (quarters or years) are available, it is customary to include dummy variables for each time period to capture influences on office lease rates from changes in overall market conditions. So for example, if available lease-transaction data involve properties that are dispersed within n submarkets and cover a time span of m periods, then (16.4) should be expanded to include n-1 submarket dummies and m-1 year dummies. Usefulness Hedonic office-rent models can help directly or indirectly in developing estimates of the following: achievable lease rates for the planned office space that can provide the basis for developing asking-rent strategies and project rental revenue forecasts marginal values for several structure and project attributes that can provide the basis for selecting the rent-maximizing set of features and optimizing office project design residual office land value estimates (RLV) using (16.5) and (16.6) below, where P denotes office property price and C non-land office development costs RLV = (P-C) FAR (16.5) Following the simple income capitalization approach to value, the price P of the office property under consideration can be calculated as: P = NOI/CAP RATE (16.6) NOI is the Net Operating Income of the property (which is a function of lease rates, lease rollover schedules, and operating expenses) and CAP RATE is the market capitalization rate 97 The inclusion of the contemporaneous vacancy rate as an independent variable may be introducing a simultaneity bias, in that rents are not only affected by vacancy rates, but they also influence vacancy rates. For example, an office building may have lower vacancy because it offers lower rates. - 447 -
Table 16.2 Office Rent Hedonic Analysis: The Data (From Clapp, J. 1987. Handbook for Real Estate Market Analysis. Englewood Cliffs, NJ: Prentice-Hall) ID RENT VAC SF SF1 FLOOR PARK TAX AUTO ACCESS STOP BASE YEAR 1 14.11 3 5,000 35,000 4 5 2.76 32 7.5 0.00 1 84 2 12.70 9 1,000 21,000 1 2 2.76 30 7.5 2.50 0 65 3 11.46 11 3,500 15,000 2 9 2.76 34 7.5 3.25 0 73 4 14.44 2 20,400 49,500 6 21 2.76 28 7.5 0.00 1 68 5 15.01 0 7,800 27,000 3 19 2.76 31 7.6 2.10 0 79 6 12.29 18 3,000 10,500 2 9 2.35 26 8.1 4.50 0 54 7 8.18 4 4,000 13,000 1 15 1.90 23 10.0 0.00 1 64 8 11.65 20 2,500 8,200 1 11 1.90 23 10.0 5.50 0 71 9 12.14 0 1,000 6,000 1 4 1.90 21 10.8 2.00 0 65 10 13.05 8 4,000 11,000 2 20 1.90 25 10.8 3.75 0 68 11 9.44 5 1,000 13,000 2 4 1.90 26 10.5 0.00 1 76 12 8.01 3 2,500 9,000 1 11 1.90 22 10.5 0.00 1 82 13 12.20 13 6,000 22,000 3 8 2.23 28 4.5 3.00 0 80 14 13.40 0 1,000 28,000 4 2 2.23 33 4.6 0.00 1 77 15 11.49 0 4,500 18,000 2 12 2.23 31 4.8 0.00 1 75 16 9.76 9 5,000 15,000 2 16 2.23 30 4.8 2.20 0 63 17 11.20 3 3,600 12,000 1 10 2.40 29 5.0 0.00 1 71 18 11.02 6 2,500 14,000 2 9 2.40 29 5.0 0.00 1 78 19 11.45 9 1,000 8,000 1 4 2.60 32 7.8 3.10 0 68 20 11.42 12 3,000 12,000 2 15 2.60 30 8.0 4.25 0 72 21 10.85 10 1,000 14,000 1 5 2.60 28 8.1 3.60 0 83 Variable Explanations Lease Terms and Characteristics RENT : Base rental rate per year per square foot of floor space STOP : Stop clause for common expenses BASE : Base year escalation clause FLOOR : Floor in building for tenant Building/Unit Characteristics VAC : Building vacancy rate SF : Net rentable square footage occupied by tenant SF1 : Net rentable square footage in building TAX : Effective tax rate AGE : Age of the building (87 - Year build) PARK : Number of parking spaces assigned to tenant Location Characteristics AUTO : Average auto time to the building ACCESS : Average distance to the CBD - 448 -
optimal office project development density, that is, the density most likely to maximize residual land value (see Chapter 9 for an elaborate discussion of how the results of hedonic analysis can be used to calculate optimal development densities) Critique Contrary to the competitive differential technique, the impact (weight) of the various office project and location attributes, as well as lease characteristics, on office space rents is not assumed in the hedonic approach, but derived in an objective and reliable way through rigorous statistical analysis of real market data. The complex, multi-year, and multidimensional nature of non-residential lease contracts, in general, and office leases, in particular, makes the application of this technique even more necessary. Notice that hedonic valuation techniques allow for an objective estimation of the effect of location and structure attributes on office rental rates, while controlling for variations in a multitude of lease terms and vice versa. The major limitation of this technique is that it requires the collection of data on a considerably greater number of office comps and lease transactions than the one required for the simple competitive differential technique. The scarcity of systematic, comprehensive, and consistent data on actual office lease transactions renders the application of this approach more difficult. Developing Office-Project Absorption and Revenue Schedules Office project absorption depends on the macro conditions prevailing in the metropolitan area and submarket within which the property is located and its competitive strength (determined primarily by its relative structure and location amenity levels). Within this framework, project absorption analysis brings together the findings from the macro- and micro-analysis stages discussed so far. Office space absorption for a specific project can be calculated using the same general formula utilized in the case of residential project analysis and described by (16.7): Project Capture = (NRA S / NRA M ) * CPI S * AB M (16.7) where NRA S : the subject s net rentable area (NRA) in square feet that is available for leasing NRA M : net rentable office square footage in the market expected to compete with the project CPI S : the subject s Competitive Position Index calculated as the ratio of the subject s weighted (unweighted) total amenity index over the average of the weighted (unweighted) total amenity indices for all properties considered in the analysis (including the subject) AB M : anticipated market absorption - 449 -
Red Flags In applying formula (16.7) for the estimation of the absorption of a planned office project, the analyst is cautioned to pay attention to the following points: The figures used for the all four terms in (16.7) should not refer to the period during which the market study is carried out, but to the period for which absorption analysis is performed. The subject s net rentable area available for leasing, NRA S, will, most likely, not be equal to the total NRA of the planned project if the period for which absorption analysis is performed is one or more years after the completion of the project. The subject s net rentable area available for leasing, NRA S, will certainly not be equal to the total NRA of the planned project during the year of market entry if some office space has been pre-leased. The estimate of the project s Competitive Position Index, CPI S, for the anticipated year of completion needs to account for all competing office projects that are in the planning stage or under construction and are expected to be completed during the same year as the subject. Unless no new competing office space is expected to enter the market after the planned project is completed, CPI S should be declining during the subsequent years. The analyst needs to make sure that NRA M, CPI S, and AB M are consistent in terms of the geographic area of reference and office building type (see elaborate discussion of this issue in Chapter 9). For example, if CPI S represents the project s competitive position relative to competing class A office properties in the project s submarket, then NRA M and AB M must also represent total available square footage and expected net absorption of class A office space in the project s submarket, respectively. Using available square footage and expected net absorption of class A and B office space in the metro area instead could lead to inaccurate estimates of the project s absorption. Some practitioners derive project absorption rates by adjusting the absorption rates of comparable office properties according the project s competitive position relative to each comparable. In particular, such estimates can be derived by multiplying the project s relative amenity indices with the absorption rates of the respective comparables (see discussion on Chapter 9 on how to estimate relative amenity indices). This methodology is the same as the one used in applications of the competitive differential technique for the estimation of the project s lease rate. As in the case of lease rates, absorption estimates through this methodology are problematic because, by construction, must refer strictly to the time of analysis and need to be adjusted accordingly for changes in market conditions expected to take place between the time of the study and actual project market entry, as well as over the project s lease-up period. 98 Using formula (16.7) is a better approach for estimating office 98 These estimates can not refer to the time of project completion simply because market studies are carried out well before project construction starts, let alone completed. Thus, the only absoprtion rates that the analyst can use are those of comparable office buildings that were completed at least within 12-24 months prior to the time of the study. Assuming a two-year period from the time the market study is carried out and the time the project is completed, and another two years until the building leases up and attains a relatively stabilized occupancy, project absorption estimates using the competitive differential approach need to be carried forward for at least - 450 -
project absorption, because it does take into account the demand-supply conditions expected to prevail at the time of project entry. 99 Table 16.3 presents an example of office project absorption and revenue schedule calculations. The notes in the table explain clearly the different entries and the calculations and estimation procedures involved. Notice that in this example, the methodology relates project absorption to total metro absorption. However, as mentioned earlier, often project absorption is calculated using submarket absorption estimates as framework. It should be emphasized that the formulas for calculating project absorption and revenue schedules are the same independently of whether what is labeled in Table 16.3 as market absorption and competing available supply refers to the submarket or the metro area. To help the reader better understand the figures presented in this table we elaborate on the calculations for 1998. Project absorption (column [7]) The rationale underlying this calculation is that the project s share of total market absorption (column [2]) will be equal to its fair market share adjusted for any competitive advantages or disadvantages that may enhance or diminish its appeal. Following (16.7) project absorption is therefore calculated as: Project absorption 1998 = Minimum of [ (32,713/1,252,160)*1*1,325,021] and 32,713 = = Minimum of [ 34,616 and 32,713] = 32,713 Since expected market absorption exceeds available supply in 1998, the fair market share of the project would be greater than the space available in the building and, therefore, project absorption will be equal to the available space. Now the reader may be wondering how it is possible for net office space absorption to be greater than the market s available supply. The reader is reminded that the so called structural vacant stock, which is derived based on the assumption of a structural or normal vacancy rate, is not included in the calculation of an office market s available supply. So obviously, what the numbers for 1998 suggest is that some of that structural vacant stock was absorbed in order to accommodate excess demand. Two points need to be made regarding the project s Competitive Position Index in Table 16.3. First, it should be noted that an average Competitive Position Index of 1 (as opposed to greater than 1) is not necessarily unreasonable for a newly build project. A newly build project can reasonably be at an average competitive position for a number of reasons. For example, the construction and design quality of the building, as well as amenities provided by the project, may be inferior to those of other new projects or even existing buildings. Furthermore, the project s location may not be as attractive and advantageous as those of other competing projects. three or four years. This task requires supply-demand equilibrium analysis of the project s market over the forecast period either through econometric or accounting techniques. Equation (16.7) does account for the demand-supply equilibrium conditions expected to prevail over the forecast horizon. 99 As explained earlier, project rent estimates through the competitive differential technique can be carried out forward by applying forecasts of market-rent growth rates. - 451 -
Table 16.3 Example of Office Project Absorption and Revenue Schedule Calculation Market Project First-Year Compe- Competing Lease Structure titive Project Project Gross Cumulative Market Available Rate Available Position Absor- Lease Rental Gross Year Absorption Supply Index Space Index ption Rate Revenue Revenue [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] 1997 1,050,939 1,444,800 138.06 120,000 1 87,287 $31.01 $2,706,770 $2,706,770 1998 1,325,021 1,252,160 154.63 32,713 1 32,713 $34.73 $1,136,122 $3,897,028 1999 1,010,845 1,651,200 173.18 0 1 0 $38.90 $3,974,968 2000 1,016,312 1,135,200 176.65 0 1 0 $39.68 $4,054,468 2001 1,326,922 412,800 181.95 0 1 0 $40.87 $4,135,557 Note: Anticipated time of entry is year-end 1996; the annual rent escalation is 2% and the length of all leases is 5 years On Inputs and Calculations [1] Year [2] Market (net) absorption; estimated through an absorption regression model utilizing as independent variables, among other factors, office employment growth (provided by an econometric forecasting firm) and real rents (see Chapter 15 for a discussion of the econometric approach) [3] Competing available supply of office space; also estimated through the econometric approach (see Chapter 14 for a detailed discussion of the calculation of availalble office supply) [4] Office lease rate growth index; also estimated through the econometric approach [5] Project size was determined through design specifications [6] The competitive strength of the project is assumed to be average [7] Calculated as the minimum of (([5]/[3])*[6]*[2]) and [5] [8] The year-end 1996 hedonic lease rate for the project was calculated through hedonic regression analysis using a sufficient sample of recent office lease transactions within the market the project competes; the lease rate for subsequent years was calculated by growing the estimated current lease rate for the structure under consideration at the growth rate dictated by [4]; the numbers reported represent the annual rate per square foot [9] Gross rental revenue refers here to the revenue only from new leases signed during the year under consideration; estimated as [8]*[7] [10] Cumulative revenue refers here to the revenue from both new and existing leases; estimated from [9] taking into account lease length and CPI escalations stipulated by lease agreements What may appear unrealistic in Table 16.3 is keeping the Competitive Position Index at 1 throughout the forecast. This maybe unrealistic as new buildings entering the market after the project is completed may be at a better competitive position than the subject. Hedonic lease rate (column [8]) The rationale underlying the estimate of the project s lease rate in 1998 is that during that year it will increase (decrease) from its 1997 level at the same rate as the market office lease rates. Within this context, it is calculated below by applying the anticipated percentage change in the overall market lease rate in 1998 to the project s 1997 lease rate (note for column [8] in Table 16.3 explains how the latter has been estimated). Project lease rate 1998 = Project lease rate 1997 * (1 + % change in market lease rate 1998) = 31.01 * (154.63/138.06) = $34.73-452 -
Gross rental revenue (column [9]) For the purposes of this example, gross rental revenue refers to the rental income attributed only to new leases, signed during the year of the analysis. In order to keep the calculations simple it is assumed that all new leases are signed on January 1 st of the year under consideration. Within this context the gross rental revenue for 1998 will be: Gross rental revenue 1998 = Square feet leased in 1998 * 1998 lease rate = 32,713 *34.73 = 1,136,122 Cumulative gross revenue 1998 (column [10]) For the purposes of this example, cumulative gross revenue refers to all the rental income earned by the property from both existing and new leases. As such, it is the sum of the rental income from new leases signed in 1998 and the income from leases signed in 1997. Notice that, according to the assumed lease specifications, the contract rate for the leases signed in 1997 will increase by 2% in 1998. Within this context, the 1998 cumulative gross revenue is calculated as: Cumulative gross revenue in 1998 = (2,706,770 * 1.02) + 1,136,122 = 3,897,028 The calculation of the project s revenue schedule is based on the assumption that all tenants in the hypothetical office building under consideration have 5-year leases. What if the tenants that occupied the building in 1997 had 3-year leases? In that case, we would first put the 87,287 square feet that were absorbed in 1997 back into column [5] (labeled structure available space ) in 2000. Then we would need to apply the same formula used to calculate the entries in the project-absorption column (column [7]) in order to estimate how much of that space would be absorbed given the anticipated demand supply conditions for that year. Another approach is to assume a renewal percentage for expiring leases, let s say 50%, and put back in the market under column [5] only 50% of the 87,287 square feet. CHAPTER SUMMARY The microeconomic analysis component of office market studies focuses on the assessment of the strengths and weaknesses of a given site and location for office development and the evaluation of the competitive position and revenue-generating ability of the planned office project. Office location analysis focuses on the site s advantages and constraints. Locational attributes that are relevant in office location analysis include all factors that may directly help firms increase their productivity and reduce their costs. Such productive amenities include access to business services, access to white-collar labor, and access to airports and freeways, as well as factors that may appeal to their workers (worker/non-productive amenities), such as low crime rates, a good public education system, high levels of public services, access to shopping and recreational amenities, etc. Locational constraints that are relevant in office location analysis include zoning, density, and growth controls, as well as any other factor that may limit the site s development potential or the overall supply of office space in the jurisdiction or county within which the - 453 -
site is located. Analyzing constraints on office development becomes especially relevant in light of theoretical findings suggesting that office location advantages are fully or almost fully capitalized on office rents as opposed to office-worker wages only in the case of supply-constrained markets. Analysis of the income-generating ability of a planned office development focuses on forecasts of achievable lease rates, absorption schedule and revenue schedule. Achievable lease rates for office developments can be estimated through the competitive differential technique or hedonic valuation techniques. The important point to be kept in mind is that, ideally, office rent estimates must be based on the analysis of actual lease transactions and not asking rents. Proper analysis of lease-transaction rates requires collection of data not only on structure and location attributes, but also on a host of lease characteristics. Hedonic valuation techniques can better measure the effect of these multiple lease characteristics on office rents, while controlling for differences across properties in structure and location attributes, and vice-versa. Office project absorption can be estimated using the fair market share concept adjusted by the project s Competitive Position Index. Project absorption and lease rate estimates provide the basis for generating the project s rental revenue schedule. In deriving this schedule, the analyst needs to apply most likely CPI clauses expected to be included in lease contracts and carefully account for potentially different lease rates for space expected to be absorbed in different years. QUESTIONS 1. Discuss what location attributes may play an important role in the intrametropolitan location decisions of office firms and why. 2. Discuss what the issue of location-amenity capitalization in the office market has to do with office project analysis. 3. List and briefly describe alternative techniques and the steps involved in estimating an office project s achievable rent and absorption schedule. 4. Describe the steps in estimating an office project s achievable rent using the competitive differential technique. 5. Discuss in detail the differences between apartment and office hedonic rent formulations. 6. You are considering to invest in a brand new office structure expected to be completed in two years, that is, year-end 1999, at a site located in one of the Los Angeles area's most vital edge cities. The market analyst has provided you with the following sets of data, which will help you assess the property's income earning potential. Set A: Forecasts of movements in real base-year lease rates during the period of 1997-2004 under a "base" scenario. These are presented below in the form of inflation-adjusted year-end indices. Year 1997 1998 1999 2000 2001 2002 2003 2004 Index 100 102 104 108 111 112 110 108-454 -
Set B. Hedonic results, based on data on 300 office lease transactions that took place in 1997 in several of the greater Los Angeles' office-commercial nodes. A preliminary hedonic analysis of this data produced the results presented in the table below. Dependent Variable: Natural logarithm of the base-year lease rate Constant 3.54 12.20 Net (Dummy; =1 if rent is net; =0 otherwise) -0.09-3.20 TripleNet (Dummy; =1 if rent is triple net; =0 otherwise) -0.25-4.20 Stop (numeric; indicates stop dollar amount) 0.02933 5.20 CPI Escalation (numeric; indicates % escalation) -0.44-5.67 Ln(Lease Length) (numeric; denotes ln of lease-term in years) -0.08-4.20 Ln(Size) (numeric; denotes ln of sq. ft. involved in transaction) -0.014-1.60 R-Squared 0.44 (a) Focusing on Set B's hedonic valuation results, interpret/explain the following: The R-squared The sign of ln(lease Length); explain whether this makes sense and why The coefficient of ln(lease Length) The t-statistic associated with ln(lease Length) (b) Compute the year-end 2004 base-year triple-net lease rate of a lease transaction involving 10,000 square feet of office space and a 4-year lease (e.g., Lease length=4 years), with a stop amount of $3.75 (e.g., stop=3.75) and a 3% CPI escalation. (c) Explain in detail how and why you would refine the above model so that it yields better estimates. Again, make sure to provide a clear rational. (d) Would, in principle, the application of the competitive differential technique be preferable? Explain clearly why or why not, based on the specifics of the case. 7. Describe in detail how you would go about estimating the absorption schedule for a planned office building. 8. Discuss how you could go about developing a 5-year forecast of the base-year lease rate for a planned office project using econometric techniques. REFERENCES AND ADDITIONAL READINGS Brennan, T., R. Cannaday, and P. Colwell. 1984. Office Rent in the Chicago CBD. AREUEA Journal 12: 243-260. - 455 -
Carn, N., J. Rabianski, R. Racster, and M. Seldin. 1988. Techniques of Performing Office Market Analysis. Chap. 12 in Real Estate Market Analysis: Techniques and Applications. Englewood Cliffs, NJ: Prentice-Hall. Clapp, J. 1987. Evaluating Competing Properties. Chap. 10 in Handbook for Real Estate Market Analysis. Englewood Cliffs, NJ: Prentice-Hall DiPasquale, D. and W. Wheaton. 1996. The Operation of Nonresidential Property Markets. Chap. 11 in Real Estate Markets and Urban Economics. Englewood Cliffs, NJ: Prentice Hall. McMahan, J. 1989. Office and Industrial. Chap. 8 in Property Development. New York, NY: McGraw-Hill Publishing Company. Peiser, R. and D. Schwanke. 1992. Professional Real Estate Development. Washington, DC: Dearborn Financial Publishing, Inc. and ULI-the Urban Land Institute. Sivitanidou, R. 1996. Do Office Firms Value Access to Office Employment Centers? A Hedonic Value Analysis in the Los Angeles PMSA. Journal of Urban Economics, 40: 125-149. Sivitanidou, R and W. Wheaton. 1992. Wage and Rent Capitalization in the Commercial Real Estate Market. Journal of Urban Economics 31: 206-229 Vandell, K. D. and J. S. Lane. 1989. The Economics of Architecture and Urban Design: Some Preliminary Findings. AREUEA Journal 17:233-259. - 456 -
APPENDIX 16A SOME EMPIRICAL RESULTS ON OFFICE FIRM LOCATION PREFERENCES The empirical results republished below from Sivitanidou (1996) demonstrate the host of location factors (firm amenities, worker amenities, and zoning constraints that were found the influence intra-metropolitan variations in office property values in the Los Angeles metropolitan area. Notice that the estimated regression models control for a variety of property-specific characteristics. Table 14.B.1 Table 14A2.1 Los Angeles PMSA Centers: Service Employment Densities, Service Employment and Sectoral Composition Sectoral Composition of Service Employment Gross Service Employment Density Total Other Professional Density Peak Service Financial Legal Support Business Centers/Center Districts (workers/mile2) (workers/mile2)b Employment c Number Percent Number Percent Number Percent Number Percent 1. CBD/Wilshire 24,429 107,482 104,091 40,976 39.37 21,221 20.39 21,446 20.6 20,448 19.64 CBD 29,224 107,482 74,202 30,575 41.21 16,811 22.66 15,423 20.79 11,393 15.00 Wilshire District 17,358 42,176 29,889 10,401 34.80 4,410 14.75 6,023 20.15 9,055 30.00 2. Century City/Beverly Hills 11,885 29,459 73,254 24,712 33.73 15,717 21.46 17,755 24.24 15,070 20.57 Century City/Beverly Hills 12,509 29,459 58,215 19,644 33.74 12,804 21.99 14,340 24.63 11,427 19.63 West Los Angeles 9,959 15,405 15,039 5,068 33.70 2,913 19.37 3,415 22.71 3,643 24.22 3. Canoga Park/Warner Center 10,091 18,624 20,189 7,508 37.19 395 1.96 11,977 59.32 309 1.53 4. Santa Monica 8,942 10,707 8,476 2,979 35.15 1,702 20.08 2,063 24.34 1,732 20.43 5. Glendale 8,633 11,635 9,423 5,564 59.05 650 6.90 1,894 20.10 1,315 13.96 6. Pasadena 8,617 9,530 15,498 8,127 52.44 1,601 10.33 3,916 25.27 1,854 11.96 7. Long Beach 8,014 8,448 5,220 2,032 38.93 851 16.30 946 18.12 1,391 26.65 8. Hollywood 7,672 13,133 9,609 1,858 19.34 766 7.97 2,536 26.39 4,449 46.30 9. Sherman Oaks 6,189 7,430 16,187 7,020 43.37 3,094 19.11 3,051 18.85 3,022 18.67 All Centers 12,556 107,482 268,258 102,459 38.19 47,127 17.57 67,456 25.15 51,216 19.09 Remaining Los Angeles PMSA 165 a NA 658,050 212,245 32.25 26,293 3.996 194,588 29.57 224,924 34.18 Los Angeles PMSA 231 a NA 926,308 314,704 33.97 73,420 7.926 262,044 28.29 276,140 29.81 Notes: a The extremely low densities here are attributable to the inclusion of census tracts with very low densities. Excluding census tracts with densities below one worker per acre raises the two figures to 1,505 and 2,260, respectively b NA stands for Non Applicable c See text - 457 -
Table 14.B.2 Variable Description Expected Variable a Sign b Description Data Source Dependent Variable (V/L) V/L NA A ssessed Property Value per unit land ($/sqft) TRWREDI Building Traits (X) AGE [ - ] Adjusted Building Age (93-Effective Year of Construction) TRWREDI AREAFL [ - / + ] Average floor area (in 000s sqft) TRWREDI FRAME {1,0} [ - ] FRAME=1 denotes metal frame TRWREDI WGLA SS {1,0} [ + ] WGALSS=1 denotes external glass walls TRWREDI WOOD {1,0} [ - ] WOOD=1 denotes external wooden walls TRWREDI ELEVATOR [ + ] Number of elevators in structure TRWREDI PARKING {1,0} [ + ] PARKING=1 denotes subterranean parking TRWREDI Center Accessibility (CENTER)c DCBD [ - ] Distance to the "main" CBD/Wilshire Center (in miles) Mapinfo Maps, 1990 DSCENTER1 [ - ] Distance to the closest of "secondary" centers Mapinfo Maps, 1990 DSCENTERN; N=2,3,..8 [ - ] Distance to the Nth-closest of "secondary" centers Mapinfo Maps, 1990 DCENTURY [ - ] Distance to the Century City/Beverly Hills Center (in miles) Mapinfo Maps, 1990 DCANOGA [ - ] Distance to the Canoga Park/Warner Center (in miles) Mapinfo Maps, 1990 DSMONICA [ - ] Distance to the Santa Monica Center (in miles) Mapinfo Maps, 1990 DGLENDALE [ - ] Distance to the Glendale Center (in miles) Mapinfo Maps, 1990 DPASADENA [ - ] Distance to the Canoga Pasadena Center (in miles) Mapinfo Maps, 1990 DHOLLYW [ - ] Distance to the Hollywood Center (in miles) Mapinfo Maps, 1990 DLBEACH [ - ] Distance to the Long Beach Center (in miles) Mapinfo Maps, 1990 DSHERMAN [ - ] Distance to the Sherman Oaks Center (in miles) Mapinfo Maps, 1990 Other (Control) Location Attributes (A) PLOCAL [ + ] Local concentration of employment in Banking, Finance, SCAG, 1990; d Legal and Business Services; measured at the 1990 Census, STF3A Files census tract level DAIR [ - ] Property's distance to the closest of major airports (in miles) Mapinfo Maps, 1990 DFREEWAY [ + ] Property's distance to the closest freeway (in miles) Mapinfo Street Maps, 1990 PRESTIGE {1,0} [ + ] A Beverly Hills dummy for location prestige Mapinfo Maps, 1990 PINCOME [ + ] Per capita income; measured at the census tract level 1990 Census, STF3A Files CRIME [ - ] FBI total crimes per 10,000 residents; measured FBI Crime Reports, 1990 at the city level RETAIL [ + ] Retail employment per resident population; SCAG, 1990 measured at the census tract level MOTION [ + ] Concentration of motion picture employees; SCAG, 1990 measured at the census tract level DOCEAN [ - ] Property's distance to the ocean (in miles); Mapinfo Maps, 1990 BEACH {1,0} [ + ] Denotes properties in oceanfront towns Mapinfo Maps, 1990 PLAND [ - ] Commercially zoned land as percent of total land; SCAG, 1990 measured at the city-district level Notes: a The symbol in parenthesis next to each variable category is the theoretical counterpart of the attribute vector b NA stands for Not Applicable c All distances were measured from each property's location to the centroid of that census tract within each center exhibiting a gross employment density peak d SCAG stands for Southern California Association of Governors - 458 -
Table 14.B.3 Descriptive Statistics Standard Variable Mean Deviation Dependent Variable (V/L) 96.11 116.87 Building Traits (X) AGE 27.29 16.73 AREAFL 5,039 6,583 FRAME {1,0} 0.01 0.11 WGLASS {1,0} 0.02 0.15 WWOOD {1,0} 0.04 0.19 ELEVATOR 0.11 0.74 PARKING {1,0} 0.05 0.23 Center Accessibility (CENTER) DCBD 12.14 6.68 DSCENTER1 6.92 4.94 DSCENTER2 11.07 7.19 DSCENTER3 13.65 6.61 DSCENTER4 16.00 6.62 DSCENTER5 18.24 7.00 DSCENTER6 20.13 7.22 DSCENTER7 23.40 6.54 DSCENTER8 30.09 7.15 DCENTURY 14.88 8.55 DCANOGA 24.59 10.95 DSMONICA 17.79 8.81 DGLENDALE 14.21 7.37 DPASADENA 15.19 6.90 DHOLLYW 13.02 7.98 DLBEACH 21.49 9.39 DSHERMAN 18.34 9.94 Other (Control) Location Attributes (A) PLOCAL 17.59 10.75 DAIR 11.83 7.17 DFREEWAY 2.77 1.96 PRESTIGE {1,0} 0.01 0.11 PINCOME 16,880 10,107 CRIME 107.74 451.56 RETAIL 19.08 68.78 MOTION 0.01 0.03 DOCEAN 12.57 7.99 BEACH {1,0} 0.05 0.21 PLAND 0.17 0.07-459 -
Table 14.B.4 Estimation Results: Model I and Model II Variables Model I-1 Model I-2 Model II INTERCEPT 3.626 *** 4.104 *** 3.299 *** (5.869) (5.865) (3.720) AGE -0.128 *** -0.087 ** -0.137 *** (-4.079) (-2.442) (-4.410) AREAFL 0.004 0.023 0.022 (0.154) (0.696) (0.771) FRAME -0.482 ** -0.537 ** -0.411 ** (-2.259) (-2.198) (-1.959) WGLASS 0.544 *** 0.488 *** 0.562 *** (3.476) (2.728) (3.653) WWOOD -0.229 ** -0.284 ** -0.212 * (-1.926) (-2.096) (--1.824) ELEVATOR 0.135 *** 0.140 *** 0.134 *** (4.171) (3.800) (4.208) PARKING 0.540 *** 0.747 *** 0.512 *** (4.913) (5.993) (4.704) DCBD -0.367 *** - -0.235 *** (-10.968) (-3.720) DSCENTER1-0.173 *** - -0.098 *** (-5.968) (-2.677) DSCENTER2 - - -0.101 (-1.550) DSCENTER3 - - -0.233 (-1.126) DSCENTER4 - - -0.809 *** (-2.833) DSCENTER5 - - -0.076 (-0.235) DSCENTER6 - - 0.719 (2.152) DSCENTER7 - - 0.196 (0.528) DSCENTER8 - - 0.248 (1.036) PLOCAL 0.007 *** 0.015 *** 0.006 ** (2.827) (5.586) (2.549) DAIR -0.022 ** -0.032 *** -0.004 (-2.143) (-2.780) (-0.307) DFREEWAY 0.017-0.005 0.019 (0.576) (-0.155) (0.644) PRESTIGE 0.478 ** 0.622 *** 0.340 (2.346) (2.672) (1.548) PINCOME 0.187 *** -0.040 0.125 ** (3.512) (-0.712) (2.251) CRIME -0.104 ** -0.037-0.131 ** (-1.936) (-0.599) (-2.370) RETAIL 0.148 *** 0.174 *** 0.154 *** (5.74) (5.902) (5.857) MOTION 0.026 *** 0.038 *** 0.009 (3.762) (4.925) (1.221) DOCEAN -0.063 *** -0.064 *** -0.079 *** (-5.449) (-4.86) (-5.897) PLAND -0.114 *** -0.196 *** -0.044 (-2.514) (-3.977) (-0.858) R 2 0.59 0.45 0.61 R 2-Adjusted 0.57 0.44 0.59 F-Statistic 38.47 25.33 30.86 Note: t-statistics are reported below the coefficients; one, two, and three asterisks denote significance at the 0.10, 0.05, and 0.01 levels, respectively; t-statistics based on White's [35] consistent covariance matrix are extremely close to the ones reported here. - 460 -
Table 14.B.5 Estimation Results: Model III Variables Model III-1 Model III-2 INTERCEPT 4.767 *** 4.746 *** (7.036) (7.003) AGE -0.123 *** -0.124 *** (-3.955) (-3.980) AREAFL 0.022 0.023 (0.776) (0.821) FRAME -0.391 * -0.412 * (-1.854) (-1.954) WGLASS 0.503 *** 0.508 *** (3.268) (3.296) WWOOD -0.181-0.170 (-1.558) -1.465 ELEVATOR 0.124 *** 0.123 *** (3.900) (3.875) PARKING 0.596 *** 0.603 *** (5.435) (5.493) DCBD -0.212 *** -0.207 *** (-4.387) (-4.282) DCENTURY -0.385 *** -0.378 *** (-3.226) (-3.173) DCANOGA -0.186 *** -0.187 *** (-3.285) (-3.301) DSMONICA 0.149 0.084 (1.183) (0.698) DGLENDALE -0.169 *** -0.167 *** (-2.683) (-2.634) DPASADENA 0.015 0.029 (0.335) (0.657) DHOLLYW 0.025 0.026 (0.394) (0.411) DLBEACH -0.047-0.095 ** (-1.009) (-2.228) DSHERMAN 0.045 0.048 (0.694) (0.748) PLOCAL 0.008 *** 0.008 *** (3.228) (3.295) DAIR 0.191 0.003 (0.894) (0.264) DFREEWAY 0.004 0.044 (0.396) (1.349) PRESTIGE 0.040 0.177 (1.211) (0.825) PINCOME 0.123 ** 0.132 ** (2.179) (2.352) CRIME -0.172 *** -0.172 *** (-3.031) (-3.025) RETAIL 0.180 *** 0.180 *** (7.003) (7.041) MOTION 0.012 0.012 (1.592) (1.526) DOCEAN (BEACH) -0.069 *** 0.539 *** (-4.486) (4.387) PLAND -0.026-0.026 (-0.488) (-0.492) R 2 0.61 0.61 R 2 -Adjusted 0.59 0.59 F-statistic 25.53 30.51 Note: See note in Table 4; also DOCEAN in Model III-2 is replaced by BEA CH - 461 -