Why do buyers pay more than what the house is worth? A Case Study of Singapore s Housing Market

Transcription

1 IRES IRES Working Paper Series Why do buyers pay more than what the house is worth? A Case Study of Singapore s Housing Market Nai Jia Lee & Qian Yi Yeo October 2013

2 Why do buyers pay more than what the house is worth? A Case Study of Singapore s Housing Market By Lee Nai Jia* and Yeo Qian Yi National University of Singapore School of Design and Environment Department of Real Estate Abstract Managing market sentiments in the residential property market is a constant challenge faced by policy makers around the world. This is especially so the Governments identify the signs of overheating. Using Singapore as a case study, this paper attempts to identify the factors that motivate homebuyers to pay prices higher than valuations. We consider that both buyers and sellers know the valuation of a flat prior to the price negotiation. The secondary market for public housing in Singapore serves as a good laboratory for us to test these factors, as Singapore s institutional framework helps to circumvent several endogeneity problems that have plagued past empirical studies. Our empirical results show that buyers tend to pay higher premiums over valuations when the subject properties are located near transport nodes and amenities. In addition, buyers tend to pay above valuations when the market is booming. These results support the that valuation reports hardly dampen the investment value perceived by buyers and sellers. They also suggest that any disclosure of market information and improvement of market transparency is unlikely to affect how individuals perceive the real estate market. * Corresponding author. Address: National University of Singapore, Department of Real Estate, 4 Architecture Drive, Singapore

3 1 Introduction The housing market is characterised by booms and busts. In the United States, the last observed boom started in 1997 and peaked in 2006 (see Figure 1). In 2007, the widespread default of subprime mortgages triggered a series of chain reactions that caused the biggest recession in the United States since the Great Depression. Asia s housing market experienced a similar series of booms and busts. In 1997, several emerging and developed economies in Asia, such as that of Singapore, suffered one of the worst financial crises caused by a sudden movement of capital flows. The same countries have experienced a housing boom since China s rapid economic development has spurred the region s economic growth, and Singapore has become a key trading partner in the Asian region for imports and exports. Asia s economic growth has initiated the recovery of the property market. This sudden turn of events is depicted in Figure 2, which describes the price movements of Singapore s private housing market. These price cycles pose political challenges for the government. At the upturn of the price cycle, the increase in housing wealth is accompanied by higher consumption of non-housing goods and borrowing of larger loans. The government needs to manage the inflationary pressure and ensure the viability of the financial system due to the increased borrowing. When the housing market is not doing well, the government needs to refinance the bad loans. In addition, they need to manage their spending with less public funds from property tax. Figure 1: US Case-Shiller Repeat Sales Index Index 50 0 Dec 31, 1890 Dec 31, 1896 Dec 31, 1902 Dec 31, 1908 Dec 31, 1914 Dec 31, 1920 Dec 31, 1926 Dec 31, 1932 Dec 31, 1938 Dec 31, 1944 Dec 31, 1950 Dec 31, 1956 Dec 31, 1962 Dec 31, 1968 Dec 31, 1974 Dec 31, 1980 Dec 31, 1986 Dec 31, 1992 Dec 31, 1998 Dec 31, 2004 Dec 31, 2010 Source: Case Shiller Price Index and Standard & Poor 2

4 Figure 2: Singapore Private Property Price Index ( ) Source: URA REALIS Researchers have argued that one of the key causes of booms and busts in the housing market is the inability of buyers and sellers to estimate house prices correctly. It is not surprising that homebuyers overpay for properties, as the housing market is hindered by high transaction and information costs. What is puzzling is that this occurs despite both buyers and sellers knowing the valuations of houses before they complete the transactions. Valuations are performed and provided by independent appraisers to determine the mortgage amounts that the buyers can borrow. According to the Royal Institution of Chartered Surveyors, the market value is defined as follows: The estimated amount for which an asset or liability should exchange on the valuation date between a willing buyer and a willing seller in an arm s length transaction after proper marketing and where the parties had each acted knowledgeably, prudently and without compulsion. 3

5 Given that buyers and sellers acknowledge an appraiser s value of a subject property, we expect the difference between price and valuation to be random after controlling for every other factor. The chance of meeting a generous prospective buyer or a hard-bargaining seller comes down to pure luck. However, anecdotal evidence suggests that the difference between price and valuation in the secondary public housing market has remained consistently positive since In a booming market, it may seem intuitive that high demand and low supply allow sellers to charge high cash-over-valuations. However, if the classical assumptions of buyers and sellers are not violated, valuations should reflect the market conditions with a high market value. Any excess cash premium is probably caused by a temporary exogenous shock, and a price should be equal to the valuation at equilibrium. We should similarly not expect properties to be sold consistently below valuation in a down market, as valuations should reflect such a market s declining conditions. We attempt to solve this puzzle by examining the revelation of a valuation on a final transacted price, and attempt to discover why buyers and sellers agree on prices that are persistently higher than valuations. We suspect that behavioural theories can explain why buyers pay high cash premiums over valuations. First, it is likely that persistent high cashover-valuations result from buyers irrational expectations of future price appreciations that are further capitalised into an agreed-upon price. In a footnote, Seiler et al. (2013) suggest that valuation does not equate to price. Although a price reflects the perceived capital appreciation of both the buyers and sellers, a valuation reflects only the current market value. In a similar paper to ours, Neo et al. (2008) argue that homebuyers set their reference values based on the location of a property and make insufficient adjustments to the final price, leading them to overpay on properties. In our paper, we have a cleaner test that examines how the anchoring process is applied to valuations to determine a final transacted price. Second, we suspect that the amount of an agreed-upon cash-over-valuation may be influenced by how market agents code, categorise and evaluate events. A cash-over-valuation may simply reflect the transaction utility of a house or its perceived value. In other words, buyers are willing to pay high cash premiums when they believe they are receiving a good deal on purchasing a unit that is close to certain amenities. Meanwhile, such amenities are accounted for in valuations, and the excess premium payable is hard to justify. According to classical economic assumptions, the way buyers and sellers conduct their mental accounting 4

6 is likely to lead them to pay more excess premiums for bigger flats compared with rational agents. Finally, the revelation of a valuation may provide information that influences the bargaining position of a homeowner. A property with a high reported value may suggest its scarcity and thus strengthen the bargaining position of the seller, who would be able to justify the high valuation for a higher premium. In contrast, the valuation may not influence the reference value that the sellers and buyers have in mind. Instead, the buyers and sellers may believe that the valuation does not sufficiently capture the amenities cash premium required to achieve their reference values. To test our hypotheses, we use a comprehensive database of housing market transactions conducted in Singapore s public housing market from 1998 to Focusing on Singapore s public housing market helps us overcome some of the empirical problems that have been inherent in the literature. For instance, the study by Neo et al. does not reflect the excess premium caused by anchoring effects, as location proxies may reflect the unique features of units located in the prime districts (Haurin et al. 2010). The prime districts have fewer public housing precincts than the other districts, and the amenities that are close to the houses in the prime districts are unique. In addition, empirical studies on the behavioural patterns of homebuyers and sellers rely on transactions involving houses with different designs and attributes. Using the hedonic regression method to uncover such attributes without a large transaction database may lead to an omitted variables bias (Watkins 1999). However, having a large database may create technical problems in forming the valuations. Using the algorithm created to find the valuation of each transacted property in the database would be too time-consuming. The public housing market transaction database helps us circumvent these technical problems. It contains over 100,000 transactions that allow us to use the hedonic regression design. Further, most of the units are similar in design, and the public housing precincts have similar amenities and facilities. The differences between the valuations derived from the valuation algorithms determined by the literature are nominal. Finally, the regulations governing the public market help decrease the need to identify proxies for information asymmetry. 5

7 In addition to having a better dataset with which to empirically examine our hypotheses, this paper makes several contributions to the literature. First, it extends how valuation disclosure affects the cash premiums paid to sellers. Previous studies have focused on how agents form myopic expectations of house price returns (Case and Shiller 1994). To the best of our knowledge, no study has explored whether the disclosure of agreed-upon premiums in a district affect the formation of house price returns. Second, it expands our understanding of why market agents agree on final prices and excess cash premiums. Our results show that the cash-over-valuations agreed upon by buyers and sellers tend to be sticky over time, providing support for the anchoring. Furthermore, it finds that the cashover-valuations for bigger types of units are higher than those for smaller units. Cash premiums also increase when the units are closer to amenities and transportation hubs. This further suggests that the context in which purchase decisions are framed affects the final payable premium amounts. The remainder of this paper is structured as follows. We begin by providing an overview of the public housing market in Singapore. We then review the literature on buyer behaviour in the housing market and other asset markets to provide a context for our tested hypotheses. We present our data and discuss empirical specifications and methodological issues. Finally, we present our results and discuss their implications. 6

8 2 Singapore s Housing Market and its Stylised Facts The division of Singapore s housing market into public and private housing sectors is its defining characteristic. The public housing sector, which is closely monitored and regulated by the housing authorities, supplants the role of the residential rental market seen in other countries. It provides housing for low- to middle-income homeowners, and helps them move up the housing ladder. First-time homebuyers can purchase new flats at subsidised rates and pay the down payments with their social security savings and cash. In addition, the government ensures that most low- or middle-income homeowners can afford new housing by setting low housing prices and low interest rates on public loans. Individuals who cannot afford the cheapest public housing available can apply for state-offered rental housing, where the rent is made affordable for the lowest income. Large subsidies are offered to buyers of public housing, and restrictions are imposed to prevent rent-seeking behaviour. For instance, individuals who cash out subsidies by selling their public housing are required to pay back a levy of 20% of the house price before they can purchase a new public housing unit from the housing authorities. First-time homebuyers cannot own a property, and their total household income cannot exceed the income ceiling. The private housing market also caters to the demand for upper-middle- to high-income households. The government does not cast restrictions on buyers other than in cases of foreign ownership of landed properties in Singapore. That said, the government intervenes in the private market through zoning ordinances, property taxes, government land sales programmes, stamp duties and lending regulations. Unlike in most countries, there is a secondary market for public housing in Singapore. The function of the secondary public housing market is similar to that of the private housing market, except that both buyers and sellers are required to fulfil several eligibility conditions before transactions can take place. Sellers must satisfy a minimum occupation period 1 before they can sell. In addition, they can sell to other buyers of other races only if the racial quota for the precinct in which the subject property is located has not been reached. Although foreigners are not allowed to purchase public housing in the secondary market, permanent 1 The minimum occupation period changes over time depending on the state of the market. Before 2011, the minimum occupation period for households using subsidies to purchase resale units was 2.5 years. Households that used their equity and housing loans from private banks to purchase the resale units were permitted to sell the properties in the secondary market after fulfilling the 1-year minimum occupation requirement. In 2011, to curb speculation, the government imposed a minimum 5-year occupation period for any unit purchased after

9 residents of Singapore can purchase it if the proportion of permanent residents on the block does not exceed 5% of the total residents. Singaporeans can apply for subsidies and public housing loans for flats purchased in the secondary market. However, they must fulfil the same conditions in the primary market to qualify. The prices for public housing in the secondary market have gone through several cycles (see Figure 3). The market crashed in 1997 due to the Asian Financial Crisis, and did not pick up until The latest housing boom, which started in 2006 and continues today, is driven by high economic performance and low interest rates. The prices in the public housing market clearly mirror those in the private housing market, suggesting a close relationship between both markets. Ong and Sing (2002) find that socially upgrading households from the public market to the private market helps to link the markets. However, they do not find any evidence that private homeowners cash out their private housing equity and move down to public housing. Their story is consistent with the Singapore Census, which has reported that less than 6% of private home dwellers downgrade to public housing. Figure 3: Resale Price Index of HDB Resale Flats Source: Housing and Development Board, 2012 During the boom in 2006, many buyers in the secondary public housing market had to pay sellers cash-over-valuations to purchase flats. A cash-over-valuation is a cash payment 8

10 made to the seller in addition to the down payment required to purchase the flat. We refer to the Housing Development Board (HDB) flat transaction process, depicted in Figure 4, to further explain cash-over-valuations. When sellers decide to sell their houses, they or their agents engage appraisers to value the properties. A valuation report is issued to determine a mortgage, and the loan amount a buyer can take out depends on the valuation. The valuation report is furnished to prospective buyers who come to view the house for sale. After visiting the house, the prospective buyer negotiates with the sellers on the price of the house. The difference between the agreed-upon house price and the valuation is the cash-over-valuation that the buyer agrees to pay, conditional on the agreed-upon house price being higher than the valuation. Once a prospective buyer agrees on a price with a seller, the buyer pays an option fee that cannot exceed $1,000. The option-to-purchase period lasts 14 calendar days and ends at 4 pm. The prospective buyer pays a fee to exercise the option. The option fee and the option exercise fee cannot exceed $5,000 and must be more than $0. The buyer and seller then arrange two appointments with the HDB to set up the property transfer. Figure 4: Transaction Process of an HDB Flat Step 1 The seller engages an agent and lists the property on the public housing resale market. The agent seeks a valuation report for the property. The listing may not include the valuation. Step 2 The prospective buyer visits the subject property for sale and acknowledges the valuation report. The prospective buyer negotiates with the seller on the final transaction price. Step 3 The prospective buyer pays the option fee. The buyer has the opportunity to refuse the option if desired. The seller cannot negotiate with other prospective buyers at this point. The buyer executes the option by paying the deposit. The buyer and seller arrange two appointments with the HDB to complete the transfer. The HDB first published the median cash-over-valuation in 2007 on its website due to the media attention surrounding the high cash-over-valuations payable and the push towards greater transparency. Figures 5 and 6 show a significant increase in the cash-over-valuations 9

11 payable from the second quarter of 2007 to the first quarter of 2013 for every housing type and area. For instance, the median cash-over-valuation for a three-room HDB flat located in a town near the CBD is about $22,500 (Kallang Whampoa) in By 2013, the median cashover-valuation rises to $53,000 for the same type of flat in the same town. Examining the cash-over-valuations within each quarter, we can see that the median cash-over-valuation tends to be low in the areas that are far from the central districts. Figure 5: Median Cash-Over-Valuations for Resale Cases Registered in the First Quarter of 2013 Town One- Two- Three- Four- Five- Executive Room Room Room Room Room Ang Mo Kio - * $30,000 $40,000 $50,000 * Bedok - * $30,000 $36,400 $45,000 * Bishan - - * $50,000 * * Bukit Batok - - $25,000 $34,000 * * Bukit Merah * * $30,000 $50,000 $69,000 - Bukit Panjang - - * $25,000 $35,000 * Bukit Timah - - * * * * Central - * * * - - Choa Chu Kang - - * $26,900 $25,500 * Clementi - - $27,500 * * * Geylang - * $26,000 $41,000 * * Hougang - - $25,000 $32,000 $40,000 * Jurong East - - $25,000 $32,000 $47,500 * Jurong West - * $25,000 $27,000 $33,000 $36,000 oa Kallang/Whamp - * $30,000 $53,000 * * Marine Parade - - * * * - Pasir Ris - - * $35,000 $40,000 $59,500 10

12 Punggol $43,000 $32,500 * Queenstown - * $27,000 $69,000 * - Sembawang $28,000 $33,000 $40,000 Sengkang - * * $38,500 $35,000 $39,000 Serangoon - * * $44,000 * * Tampines - - $27,500 $35,000 $43,000 $67,500 Toa Payoh - * $32,000 $55,000 $75,000 * Woodlands - * $27,000 $30,000 $33,900 $57,000 Yishun - - $26,000 $30,900 $38,000 * Source: HDB Figure 6: Median Cash-Over-Valuations for Resale Cases Registered in the Second Quarter of 2007 Town One- Two- Three- Four- Five- Executive Room Room Room Room Room Ang Mo Kio - * $5,000 $15,000 $25,000 * Bedok - * $7,000 $7,000 $12,800 $28,000 Bishan - - $15,000 $19,300 $28,500 $28,800 Bukit Batok - - $5,000 $9,000 $16,500 $12,000 Bukit Merah * * $15,900 $25,000 $40,000 - Bukit Panjang - - $2,000 $6,000 $0 $2,500 Bukit Timah - - * * * * Central - * $18,500 * * - Choa Chu Kang - - * $5,000 $5,000 $6,500 Clementi - - $7,300 $18,200 * * Geylang - * $8,000 $15,000 $0 * Hougang - - $9,000 $9,000 $15,000 $11,000 Jurong East - - $7,000 $10,000 $10,000 $20,000 Jurong West - - $5,000 $7,000 $6,000 $0 11

13 oa Kallang/Whamp - * $8,000 $22,500 $36,500 * Marine Parade - - $20,500 * * - Pasir Ris - - * $5,000 $6,000 $10,000 Punggol $3,000 $5,000 * Queenstown - * $14,000 $35,500 $42,000 * Sembawang $8,000 $6,000 $0 Sengkang $6,000 $5,000 $0 Serangoon - * $9,000 $12,000 $11,500 $10,000 Tampines - - $7,000 $8,000 $10,000 $15,400 Toa Payoh - * $13,000 $22,000 $34,500 * Woodlands - * $3,000 $4,000 $0 $0 Yishun - - $5,500 $5,000 $2,000 $1,400 Source: HDB Cash-over-valuations have generated much interest in understanding the rationale behind high housing costs. No study has thoroughly examined the causes of cash-overvaluations. The structure of the secondary public housing market allows us to better test the behavioural economic theories. The market s restrictions ensure a stronger consumption motive among its buyers compared with buyers in the private housing market. Its citizenship requirements remove the possibility of information asymmetry among buyers, which further helps us isolate the anchoring and framing influences. More importantly, the homogeneity of public housing and the similarity of public housing estate plans allow us to overcome the possible biases that prevent the hedonic regression method from determining a valuation. 12

14 3 Literature Review The housing and behavioural economics literature offers several possible theories that explain high cash-over-valuations. First, less-informed buyers are likely to overpay for a property compared with informed buyers (Miller et al. 1998, Lambson 2004, Watkins 1998, Turnbull and Sirmans 1993, Myer et al. 1992, Neo et al. 2008). Such an information asymmetry is created by buyers who are inexperienced in purchasing housing or who lack sufficient knowledge of a neighbourhood s institutions and attributes. Hence, an influx of migrants and foreign investors cause prices to rise above valuations. Many studies test this using the hedonic regression method to examine whether the prices paid by locals and foreigners for similar properties during the same period differ. In a recent empirical paper, Neo et al. (2008) compare the property prices paid by foreigners and locals to test the information asymmetry in Singapore, and find strong evidence that high excess cash premiums are attributable to the buyers unfamiliarity with the market. The empirical results of studies that examine the real estate market in the United States have been mixed. Miller et al. (1998) compare the prices paid by Japanese and local buyers for properties in Honolulu and find evidence supporting the information asymmetry theory. Lambson et al. (2004) compare the prices paid by out-of-state and local buyers for similar properties, and also find empirical support for the theory. However, Turnbull and Sirmans (1993), Watkins (1998) and Myer et al. (1992) find no sufficient variation in prices to validate the theory. Second, the theories presented in the time-on-market literature suggest that the premiums reflect buyer urgency. High cash-over-valuations probably result from home sellers being more patient than buyers and accepting longer time-on-market periods for their properties (Arnold 1999, Anglin, Rutherford et al. 2003). To initiate a deal, buyers must pay excess cash premiums to the sellers. We are able to rule out the two preceding theories as possible explanations for the high cash-over-valuations observed in Singapore s public housing market. Information asymmetry is not likely to have a major effect because only Singaporean citizens or permanent residents can purchase HDB flats. The lack of atypical features in public housing neighbourhoods also makes it unlikely for any first-time buyers to offer excess premiums. The readily available transacted prices and payable premiums further decrease the possibility of information asymmetry. The amount of time a property spends on the market does not 13

15 explain its persistent high cash-over-valuations. Most public housing units are similar in configuration and have similar amenities, and studies show that nearly identical houses tend to sell relatively quickly (Haurin et al. 2010). Rather than the preceding two theories, we suspect that the high transaction costs in the housing market are attributable to the high cash-over-valuations. The roles of transaction costs in the durable goods market are best explained by Grossman and Laroque (1990), who argue that a small amount of transaction costs can make market agents adjust their consumption less frequently. In an extension of the theory, buyers have to shoulder the transaction costs of the sellers to make the deals work. The transaction costs are even higher for desirable properties because the sellers require more time and manpower to conduct a search for better properties. We use the government s policies on the maximum loan-to-value ratio and minimum occupation periods as natural experiments to test the transaction cost theory and identify the effect of transaction costs on cash-over-valuations. When buyers can take out larger loans to finance their purchases, their transaction costs fall because they do not have to come up with more equity when they sell their houses. When the minimum occupation periods are raised, the options for prospective buyers decrease, thereby raising the transaction costs in the market. In addition to high transaction costs and information asymmetry, the irrationality of buyers and sellers in the market may explain why buyers are paying cash-over-valuations for properties. Arrow (1990) suggests that the psychological models of irrational decision making posited by Tversky and Kahneman (1979) help to explain the behaviour of agents in speculative markets (e.g., Shlefier 2000). Given the volatility and cyclical nature of housing prices, many researchers have tested buyer rationality in the housing market. Case and Shiller (1999) were the first to examine how people form price expectations in the United States housing market. Based on their surveys and information on past house price movements, they find that people are irrationally optimistic about housing prices. In a more recent study, Neo et al. (2008) find that the excess returns in Singapore s private housing market are positively correlated with past returns. They attribute this finding to the buyers use of past appreciation to represent current returns, which is consistent with Case and Shiller s (1999) findings. These findings are important, as they imply that irrational expectations of future price growth will be capitalised in the transacted price during the negotiation phase. Buyers are 14

16 willing to pay cash-over-valuations because they fear being priced out of the market in the future. They are also optimistic that they will earn returns that justify the premiums. In contrast, sellers set higher asking prices and cash-over-valuations to reflect the expectations of the market. The formation of such myopic expectations can be further associated with how individuals anchor on certain reference values. According to Tversky and Kahneman (1974), individuals tend to focus on anchor values during the judgement process, and their final estimates are insufficiently adjusted away from these anchors. Northcraft and Neale (1987) ask professional real estate agents to assess residential properties after giving them detailed summaries of the characteristics of the houses. After manipulating one irrelevant variable, they find that the original listing prices described in the written materials vary by ±12% of the originally assessed market value. Seiler et al. (2013) similarly find that a property buyer s offer price is affected by his or her familiarity with the neighbourhood. Lambson et al. (2004) show evidence to support the that out-of-state buyers pay premiums due to the anchoring effect. Using transactions from Singapore s private housing market, Neo et al. (2008) find empirical evidence to support their anchoring. Their results suggest that buyers anchor the value of a property to the district in which the property is located and to the property s past appreciation. However, the proxy they use may be tainted by changes in transport costs and the time required to travel from the central location to one s workplace. Another possibility is that buyers are paying high cash-over-valuations for the transaction utility they receive from the deal. The transaction utility measures the perceived value of a deal, and is defined by Thaler (1985) as the difference between the amount paid and the reference price for the good. The transaction utility theory suggests that homebuyers are willing to pay a high premium for properties that encompass many attributes. Although the buyers may not use some of the attributes, the theory suggests that he or she will nevertheless pay for them. We attempt to test this by examining whether rich homeowners would prefer to stay near train stations. Given that rich homeowners usually drive to work, their proximity to public transport is less important, and buyers are unlikely to pay a premium for a facility they do not use. The rationale for doing so must come from the transaction utility. 15

17 The preceding behavioural theories suggest that market agents form their reference values of a subject property from its surrounding environment and circumstances. Any difference between a transacted price and a valuation reflects the adjustment made to reflect the perceived value. The higher the valuation, the less the adjustment needs to be made. In contrast, a high valuation may signal the value of a property, and further enhance the bargaining position of the seller. Thus, a high-value property helps its seller achieve a high cash-over-valuation. This paper contributes to the literature in the following ways. First, it tests the preceding behavioural theories using a unique dataset that decreases the empirical problems plaguing previous papers such as that by Neo et al. (2008). Most flats have similar designs and feature access to similar amenities. This allows us to focus on testing the behavioural theories, as the time-on-market factor is likely to be similar across properties. The market s settings also help us derive a valuation easily using ordinary least squares regression. The market s segmentation is driven by location, and that is easily controlled. In addition, the volume of sales is substantial enough to provide similar estimates to that of the sales comparison method. Second, this paper is one of the first to examine the mechanism of how prices are decided among agents with valuation information. The literature has tended to assume that buyers form their beliefs based on past market information, their experience and their familiarity with the market (e.g., Seiler et al. 2013). However, it seems to have omitted how appraiser valuations affect buyer decisions. The contents of a valuation report should temper a buyer s expectations. However, our results show that valuations, together with agreed-upon cash-over-valuations, form the reference points upon which buyers and sellers negotiate. 16

18 4 Methodology Our empirical strategy comprises two steps. The first step involves the calculation of the valuations of the transacted properties. We do not have the cash-over-valuations for each property, and must rely on our transaction samples to derive their valuations. The second step involves using the cash-over-valuations derived in the first step and regressing them against the variables to test our hypotheses. During each step, we conduct a series of robustness tests to ensure the validity of the results. We explain the steps in more detail as follows. Our sample comprises 186,654 transactions made in the public housing market from 1998 to We obtained the dataset from the Singapore Institute of Surveyors and Valuers (SISV) and Housing Development Board of Singapore. Each resale transaction is substantiated with structural information such as the level of the unit, number of rooms, floor area, lease commencement data, resale approval date, street name, block number and transacted price. We further translate the addresses on each block into zip codes to calculate their distances from amenities using ArcGIS. Given that the government conducts renewal programmes on older buildings, we further complement the dataset with information related to the dates on which the renewal programmes were implemented. The descriptive statistics of the dataset are provided in Tables 1a and 1b. Table 1a: Variables and their representation Variable Structural Attributes Storey_01~05 Storey_06~10 Storey_11~15 Storey_16~20 Storey_21~25 Storey_26~30 Storey_31~35 Storey_36~40 Floor_Area~m Room_1 Room_2 Room_3 Room_4 Room_5 HUDC Representation 1st floor to 5th floor 6th floor to 10th floor 11th floor to 15th floor 16th floor to 20th floor 1st floor to 5th floor 16th floor to 20th floor 1st floor to 5th floor 16th floor to 20th floor Floor Area (sqm) One Room HDB flat Two Room HDB flat Three Room HDB flat Four room HDB flat Five Room HDB flat Housing and Urban Development 17

19 Executive MUP_1_0 Neighbourhood Attributes Distance_CBD MRT_m Park_m Primary_Sc~m Town_Centres TopPriSch Others Age Resale_Price Year of transaction Month of Transaction Valuation Cash Over Valuation Company flat Executive flat Undergone Main Upgrading Programme Distance to CBD Distance to MRT Distance to Park Distance to Primary School Distance to Town Center Distance to Top Primary School Age of Unit Resale Price Year Property transacted Month of Transaction Valuation of Unit Cash Over Valuation that buyer has to pay Table 1b: Descriptive Statistics Variable Mean Std. Dev. Min Max Storey_01~ Storey_06~ Storey_11~ Storey_16~ Storey_21~ Storey_26~ Storey_31~ Storey_36~ Floor_Area~m Age Resale_Price Year of transaction Month of Transaction Room_ Room_ Room_ Room_ Room_ HUDC

20 Executive Distance_CBD MRT_m Park_m Primary_Sc~m Town_Centres TopPriSch MUP_1_ Valuation Cash Over Valuation Source: Author Computation The tables show that the number of transactions is evenly distributed across the years. Few high floor units (30th-40th floor) were transacted in the market because few high skyscrapers were developed. In addition, the HUDC and executive housing stocks are small compared with the three-, four- and five-room housing stocks. This is further reflected in the collected transactions. The supply of one- and two-room HDB flats was limited as the government discontinued the construction of such small units. Looking at the cash-overvaluation variable, some units were transacted at S$205,255 below valuation. We suspect that this observation is an outlier, and drop it from our subsequent regressions. Deriving the Valuation There are several approaches to deriving property valuations. The typical ordinary least squares method uses log prices as the dependent variable. The log linear functional form decreases the bias resulting from the non-normal residual (Hardin and Wolverton 1996) and makes it easier to interpret. However, there may be a selection bias (Ong et al. 2006) because the ordinary least squares regression does not account for the sales intensity and units that are not transacted. Sufi and Mian (2009) similarly show that a homeowner s propensity to spend money on housing follows the business cycle. Hence, more sales occur when prices are high. McMillen and Weber (2008) also find that property tax assessments are more accurate if more transactions take place, which in turn depends on changes in home prices. To overcome the sales intensity bias, we include the number of sales in the previous period in our cashover-valuation regression. 19

21 Although academics use ordinary least squares regression models to conduct mass valuations for research, appraisers prefer to use the sales comparison method. According to this method, the appraisers use the prices of recently completed similar sales to derive the value of a property. They adjust the prices in terms of the differences in attributes between the subject property and the comparables. Finally, they derive the value by calculating the weighted average of the subject property s values derived from each comparable. Despite the similarities between the regression and sales comparison methods, the two share some inherent differences. For the latter, appraisers attempt to find transactions that are as close to the subject property as possible. The variance among comparables is smaller. The valuation becomes less accurate if the appraisers use too many comparables. In contrast, researchers who use the ordinary least squares regression method rely on a large sample of transactions. There is a larger variance across samples and less consistent values may be provided. This method requires a large sample and accounts for market segmentation. Although the sales comparison method requires a smaller sample, the decisions involved in selecting the weights for the comparables and adjustment factors rely on experience. Researchers have attempted to unite the approaches. Colwell, Cannaday and Wu (1983) use a grid adjustment method, deriving the adjustment factors via the ordinary least squares method. Isakson (1986) uses the nearest neighbours appraisal technique (NNAT), which calculates the value through a weighted average of the actual selling price. Vandell (1991) presents a minimum variance approach for selecting and weighting the comparable properties. Gau, Lai and Wang (1992, 1994) provide a similar model, but replace the variance with the variation coefficient as the measure to be minimised. However, this model presents a challenge when the valuations of large datasets must be calculated. Our method is similar to Isakson s NNAT method (1986) but is less sophisticated. We run the valuation regression using the transactions conducted in the same district as the subject property over the past two quarters as our sample. The regression model is as follows: E(Value i,t ) = β 0 + β 1 Structural Attributes i,t + β 2 Neighborhood Attributes i,t (1) + β 3 Quarter i,t, E Value i,t = E(Value i, ) PPI T PPI t, (2) 20

22 where Quarter denotes the previous quarter in which the transaction was completed, T denotes the current period and t represents a lagged current period of either one or two quarters. After deriving the valuation for the subject property from the regression model, we adjust the value using the house price index from the Singapore housing authorities/hdb to match the time at which the property was transacted. The adjustment made is based on a perfect ex ante prediction of the future change in price. This method is not without its limitations. First, transactions completed in neighbouring districts may offer better comparables than the transacted properties located within the same district for subject properties located at the district border. To ensure that the bias is limited, we further weigh the observations based on their distance to the central business districts and other amenities and re-run our regressions. Second, our method deviates from past studies in that we do not weigh the comparables according to their similarities with the subject properties in the regression model. Rather, we drop the transactions that are completed outside the district or segment. While our method is not as sophisticated as past methods, the availability of large numbers of transactions for each block and the uniformity in its design help us obtain similar estimates to those complex models. Finally, we are not privy to information on the state of the house s condition. We attempt to overcome the resultant bias by removing the outliers in the sample, and use the house s age as a proxy of its condition. Finding the Cash-Over-Valuation and Testing the Hypotheses After adjusting the valuations from the hedonic regressions derived from the past two quarters, we calculate the difference between the valuation and the actual price of the subject property that are transacted in the current period. The equations used to derive the premium or discount and the regression model are shown as follows. To ensure that the actual cashover-valuations are reflected, we take the median cash-over-valuations for each district and conduct a t-test with those derived from the published websites. Our test results, which are not presented here, find no evidence that our median cash-over-valuations are different from those published with the official figures. Using cash-over-valuation as our new dependent variable, we re-run the log-linear regression with the list of variables in Table 2 to test our hypotheses. 21

23 Table 2: Variables for Cash Over Valuation Regression VARIABLES Representation of Variables Purpose Storey_06_to_10 Storey of unit transacted is 6th to 10th Test floor Storey_11_to_15 Storey of unit transacted is 11th to 15th Test floor Storey_16_to_20 Storey of unit transacted is 16th to 20th Test floor Storey_21_to_25 Storey of unit transacted is 21st to 25th Test floor Storey_26_to_30 Storey of unit transacted is 26th to 30th Test floor Storey_31_to_35 Storey of unit transacted is 31st to 35th Test floor Storey_36_to_40 Storey of unit transacted is 35th to 40th Test floor Floor_Area_sqm Test Floor Area of Unit Transacted Age Test Age of Unit transacted AgeSq Test Square of Age of Unit Transacted Room_1 Test 1 Room HDB flat Room_2 Test 2 Room HDb flat Room_4 Test 4 Room HDB flat Room_5 Test 5 Room HDB flat HUDC Test HUDC unit (Bigger than 5 room HDB flat) Executive Executive Unit (Bigger than 5 room HDB Test flat) Distance_CBD Test Distance to CBD MRT_m Distance to nearest Mass Rapid Transit Test Station CBD_Sq Test Distance to CBD Squared Park_m Test Distance to nearest Park Primary_Sch_m Test Distance to nearest Primary School 22

24 Town_Centres TopPriSch MUP_1_0 Valuation Lagged Sales Cold Market C6TO10 C11TO15 C16TO20 C21TO25 C26TO30 C31TO35 C36TO40 CFLAREA CAGE CAGESQ CCBD CCBDSQ CR1 CR2 CR4 CR5 CRH Distance to nearest Town Center Distance to the top Primary School Dummy Variable: 1- The unit has undergone refurbishment. 0 - It has not The valuation of the unit The number of sales of the block transacted in a year for the past period Dummy Variable : If unit is transacted during down market, dummy variable =1 Interactive Variable: Cold Market *Storey_06_to_10 Interactive Variable: Cold Market *Storey_11_to_15 Interactive Variable: Cold Market *Storey_16_to_20 Interactive Variable: Cold Market *Storey_21_to_25 Interactive Variable: Cold Market *Storey_26_to_30 Interactive Variable: Cold Market *Storey_31_to_35 Interactive Variable: Cold Market *Storey_36_to_40 Interactive Variable: Cold Market *Floor Area Interactive Variable: Cold Market *Age Interactive Variable: Cold Market *Age Square Interactive Variable: Cold Market *Distance to CBD Interactive Variable: Cold Market *Distance to CBD squared Interactive Variable: Cold Market *Room_1 Interactive Variable: Cold Market *Room_2 Interactive Variable: Cold Market *Room_4 Interactive Variable: Cold Market *Room_5 Interactive Variable: Cold Market *HUDC Test Test Test Test and Signaling hypotheses Control for Blocks with few or no sales 23

25 CRE CMRTD CPARK CPRI CTCENTER CTPRI CMUP CVAL MRTR1 MRTR2 MRTR4 MRTR5 MRTRH MRTRE Y2011 Interactive Variable: Cold Market *Executive Unit Interactive Variable: Cold Market *Distance to MRT Interactive Variable: Cold Market *Car Park Interactive Variable: Cold Market *Distance to Primary School Interactive Variable: Cold Market *Distance to town center Interactive Variable: Cold Market *Distance to Top Primary School Interactive Variable: Cold Market *MUP Interactive Variable: Cold Market *Valuation Interactive Variable: Distance to MRT station *Room_1 Interactive Variable: Distance to MRT station *Room_2 Interactive Variable: Distance to MRT station *Room_4 Interactive Variable: Distance to MRT station *Room_5 Interactive Variable: Distance to MRT station *HUDC Interactive Variable: Distance to MRT station *Executive Unit Dummy Variable that takes the value 1 if it is year 2011 Test for Transaction Utility Test for Transaction Utility Test for Transaction Utility Test for Transaction Utility Test for Transaction Utility Test for Transaction Utility Test for illiquidity The behavioural theories on anchoring and the adjustment process suggest that too much weight tends to be placed on information considered early in the judgement process. We expect homebuyers and sellers who apply anchoring and the adjustment process when forming the price of a subject property to make the final price higher when the valuation is low and vice versa. Hence, the variable value, which captures the valuation of the property derived earlier, helps us test whether the anchoring is supported. On the contrary, 24

26 if the valuation is positively correlated to the agreed-upon cash-over-valuation, it is implied that the valuation is used to strengthen the bargaining positions of the buyers and sellers. We further examine whether buyers and sellers anchor excess premiums based on the attributes of the subject properties. If the attributes significantly affect the cash-over-valuation, it is implied that the valuation report fails to help the buyer and seller adjust their reference values. If the coefficients for those attributes-related variables are significant, the anchoring theory is supported. However, cash-over-valuations may reflect the transaction utility buyers have in relation to properties. In this case, the buyers are willing to overpay because they believe they are getting a good deal, and do so despite not using the facilities. We use interactive variables to test this. We multiply the variables denoting different types of housing with the variables denoting the property s distance away from a mass rapid transit (MRT) station. Households in bigger units tend to own cars and are less likely to need public transport to travel to work. However, if the proximity of such a unit to a public transport station affects its cash-over-valuation, it is implied that the premium reflects the transaction utility. It is likely that buyers and sellers form myopic expectations of cash premiums that are similar to their expectations of housing returns as described by Case and Shiller (1999). However, we consider that buyers and sellers make use of a prior agreed-upon premium as a reference value and make adjustments from there. We test this consideration by examining the coefficients of the variables used to represent the past quarter s cash-over-valuations in the regression model. In addition to using the ordinary least squares regression method, we use the weighted least squares regression method in our test to overcome the possible sales intensity bias discussed previously. We thereby increase the weightage of better-selling units in the districts compared with the lesser-selling units. We group the observations based on the number of sales transacted in the district over the past two quarters. These transactions are used to derive the valuations of the properties in our earlier regression. The cash-over-valuation error is given as Var(ε i ) = σ 2. h i, (3) where h i represents the groups formed by sales in the district over the past two quarters. Hence, our weighted least squares regression is 25

27 y i = α. + β. h i h i 1 x i + ε i h i h i. (4) By giving greater weight to groups with high sales, the regression helps us remove the valuation error caused by lesser-selling units. If the coefficients are ultimately insignificant, it is implied that the cash-over-valuation is caused by a lack of the comparables necessary to derive an accurate assessment of the property value. 26

28 5 Results The coefficients from our valuation regression model suggest that both neighbourhood and structural attributes have significant effects on housing value. Consistent with the literature, flats that are proximate to amenities, are located at a high floor level and have large floor areas tend to fetch a premium in the market. Properties that are close to the centres of business districts are similarly more expensive, which is consistent with the urban economics literature. (See Table 4 for sample valuation regression results.) Because the valuations we derived capture all of the attributes and adjustments made to reflect the time differences, we expect any cash-over-valuation or below-valuation discounts to be random. Table 4: A regression for valuation for one of the district for observations VARIABLES Log Price Storey_01_to_ *** ( ) Storey_11_to_ *** ( ) Storey_16_to_ *** ( ) Storey_21_to_ *** ( ) LnFloor 1.129*** ( ) LnAgeSq_ *** ( ) Room_ *** ( ) Room_ *** ( ) Room_ *** ( ) Executive 0.160*** ( ) LnCBD *** ( ) LnMRT *** ( ) LnPark ** ( ) LnPriSch *** 27