DNB W o r k i n g P a p e r. Market Thinness, List Price Revisions and Time to Sell: Evidence from a large-scale housing dataset. No.

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1 DNB Working Paper No. 176 / May 2008 Marco Hoeberichts, Maarten van Rooij and Arjen Siegmann DNB W o r k i n g P a p e r Market Thinness, List Price Revisions and Time to Sell: Evidence from a large-scale housing dataset

2 Market Thinness, List Price Revisions and Time to Sell: Evidence from a large-scale housing dataset Marco Hoeberichts, Maarten van Rooij and Arjen Siegmann* * Views expressed are those of the authors and do not necessarily reflect official positions of De Nederlandsche Bank. Working Paper No. 176/2008 May 2008 De Nederlandsche Bank NV P.O. Box AB AMSTERDAM The Netherlands

3 Market Thinness, List Price Revisions and Time to Sell: Evidence from a large-scale housing dataset 1 Marco Hoeberichts Maarten van Rooij Arjen Siegmann April 2008 Abstract This paper uses a large dataset, covering more than 70% of the Dutch housing market, to analyze the relationship between market thinness, price setting behavior and time to sell. Our findings confirm the typical result that overpricing increases the time on market. In addition, we find evidence of quicker list price reductions suggesting that overpricing is part of a strategy to search for the opportunity of high revenues and to learn about the market. Moreover, we are able to confirm the theory put forward by Lazear (1986) on the relation between atypical goods and the speed of price adjustments. Sellers of atypical houses are more uncertain about the price buyers want to pay and take time to learn about the market, thereby increasing the expected time on market and the time to price revisions. Market liquidity has a positive, i.e. shortening, effect on the time to sale and leads to quicker price revisions due to the increased opportunities for learning. Keywords: market liquidity, pricing strategies, marketing time, overpricing, housing JEL-Classification: R31, D83, D12, C41, E30 1 We thank Gerbert Romijn, and seminar participants at DNB and CPB Netherlands Bureau for Economic Policy Analysis for useful comments and suggestions. All remaining errors are our own. The views expressed in this paper do not necessarily reflect those of de Nederlandsche Bank. Corresponding author, M.M. Hoeberichts, Economics & Research Division, De Nederlandsche Bank (DNB), Westeinde 1, 1017 ZN Amsterdam, m.m.hoeberichts@dnb.nl M.C.J. van Rooij, Economics & Research Division, De Nederlandsche Bank (DNB), Westeinde 1, 1017 ZN Amsterdam, m.c.j.van.rooij@dnb.nl A.H. Siegmann, VU University Amsterdam, asiegmann@feweb.vu.nl

4 1. Introduction This paper provides an empirical analysis of the relation between initial list price, price revisions and time to sell of residential housing. For most households, house sales are rare events. Nevertheless, their home is typically the most important item on their balance sheet and different selling strategies might entail substantial financial consequences. A seller in the real estate market has two goals: to sell the property for the highest possible price and to sell it as quickly as possible. The list price that the seller picks for the property is an important instrument to achieve these objectives. A low list price attracts many potential buyers but weakens the seller s bargaining position in the negotiations over the final transaction price. The determination of a realistic selling price is relatively easy when markets are very liquid and there is much information on transaction prices of similar dwellings. However, in market segments where the number of trades is limited and when the house has atypical features the seller is faced with much more uncertainty about its value and there is ample room for learning. Typically, the seller sets a list price to attract potential buyers inspecting the house, thereby gathering information about the market value of the house. When time passes by, the seller learns about the valuation of buyers and has the option to lower the list price. The basic idea of learning and list price revisions has its theoretical foundation in the seminal contribution by Lazear (1986) on pricing behavior when demand for a product is uncertain. Lazear shows that in thin markets, learning occurs at a slow speed and thus prices will be rigid. The same applies to atypical houses. Haurin (1988) suggests that houses with a greater amount of unusual features are expected to remain on the market for a relatively long period of time because search theory tells that sellers will entertain a higher number of bids to take advantage of the more dispersed valuation of atypical houses. We are able to test these hypotheses using a large-scale dataset, thereby giving us the opportunity to distinguish two separate market segments. We consider houses and apartments, where the latter are more homogenous and generally traded in more liquid markets with higher turnover rates. At the same time, the dataset is large enough to measure the effect of atypicality on the time on market and time until price revisions for both categories. The dataset employed in this paper is unique in the sense that it covers more than 70% of all houses and apartments for sale in the Netherlands; the time a house is on the market, housing characteristics and any possible list price changes are recorded. Note that the number of studies on list price revisions is limited (Herrin, Knight and Sirmans (2002) are an exception). The source of the data is Funda, an online multi-listing service that was started by NVM, the largest Dutch organization of real estate agents. By taking a weekly snapshot of all listed houses we are able to track almost 400,000 different houses and apartments over a period of two years ( ). Hence, our dataset is much larger than that used in comparable studies that often focus on a small segment of the market. Moreover, the large 1

5 dataset makes it possible to perform a duration analysis to analyze the impact of initial price setting on the time that it takes for a house to sell. This paper exploits the size of our dataset to distinguish apartments from houses, while controlling for atypicality. The advantage is that the results are not conditional upon a specific region or city, but rely on nationwide data including large cities with a dense housing population, suburban areas and the countryside. Our main conclusions are as follows. Atypicality increases the time to sell and the time to price revisions, thereby confirming the theory of Lazear that learning proceeds at a lower speed. These effects are smaller in the more liquid market for apartments. At the same time, a higher degree of overpricing increases the expected time on market and decreases the time to a price revision. Again these effects are smaller for more liquid markets. The high number of potential buyers provides the seller with a lot of scope for learning thereby explaining why apartments are usually either sold more quickly or have the list price reduced sooner than houses. The rest of the paper proceeds as follows. Section 2 discusses the underlying theory by Lazear focusing on the opportunity to use price revision strategies to learn about the valuation of a house. Section 3 reviews stylized facts in the empirical literature and formulates hypothesis on the relations, between the degree of overpricing (DOP), time-on-market (TOM) and market thinness. Section 4 describes the data and provides descriptive statistics. Section 5 discusses the empirical specification of the hedonic pricing model and the duration model used to test our hypothesis, and introduces the measures for DOP, TOM and atypicality. In Section 6, we test the hypotheses, discuss the results and present a robustness analysis. Section 7 concludes. 2. Theoretical framework The relationship between DOP and TOM is straightforward and is extensively researched in papers like Anglin, Rutherford and Springer (2003). These papers typically assume that the list price is a signal to potential buyers. If the list price is higher than the expected list price, potential buyers are less likely to visit the house. The percentage difference between the expected list price and the actual list price is the degree of overpricing. Atypical houses have a longer expected selling time. This relationship between atypicality and timeon-market is analyzed in Haurin (1988). A longer time-on-market enables the seller to learn about the dispersion in the valuation of potential buyers. Put differently (see Turnbull, Dombrow and Sirmans, 2006), there are less buyers who strongly prefer an atypical house and therefore it takes longer to 2

6 match these few buyers in the population with the atypical house that is for sale. In these papers, however, there is no explicit role for list price revisions. The theoretical starting point for the analysis of list price revisions is Lazear (1986). Lazear analyzes the problem of a retailer who wants to maximize the proceeds from selling a single product, facing a stochastic arrival process of potential buyers, when the seller is uncertain about the price buyers are willing to pay for his product. A seller of a house faces exactly such a problem. The two-period model as given by Lazear is that of a seller with reservation price R 1 in the first period and R 2 in the second. In both periods, the seller encounters exactly one client who is willing to pay V for the good, but no more. The seller does not know V with certainty, but has a prior notion of its probability distribution. The problem for the seller can thus be written as max R1[1 F( R1 )] + R2[1 F2 ( R2 )] F( R1 ), (1) R r, R2 where F(.) is the cumulative distribution function of V in the first period and F 2 (.) that of the second period. The first term is the price charged in the first period times the probability of a sale. The second term is the price charged in the second period times the probability of a sale, times the probability that the good is not sold in the first period. The model in Equation (1) is the most basic representation of the situation with one seller entering a store. However, in practice, as with a house, multiple buyers inspect a good at the same time, given an R 1 or R 2. To accommodate this, Lazear extends the model in (1) to a model in which in each period there are N customers that inspect a good, of which a fraction P are just shoppers whose value of the good is lower than the reservation price. This leads to the extended model: N N max R1[1 F( R1 )](1 P ) + R2[1 F2 ( R2 )](1 P )(1 [(1 F( R1 ))(1 P Rr, R2 N )]). (2) Note that there is still one V, the price that buyers (not being shoppers) are willing to pay for the good. Using Bayes Theorem Lazear derives the distribution of F 2 in terms of R 1, P and N, which directly leads to the following solution of optimization problem (2): R 1 N N 2 + P (1 P ) =, (3) N 2 4 (1 P ) N N [ R (1 P P ] 1 R 2 = 1 ) +. (4) 2 Equations (3) and (4) provide a number of intuitive relationships. Consider the extreme cases for P N. First, if P N =1 the solution becomes R 1 = R 2 = ½. If all customers are shoppers, no information is conveyed by not closing a sale in the first period. Hence, prices remain constant over time. Second, if P N =0 all customers are buyers and the optimal price setting is R 1 = 2/3 and R 2 = 1/3. That is, the seller 3

7 starts with a high price and cuts it in half if no sale occurred in the first period. The intuition is that if the good was not sold in the first period, it can only be that the good was priced too high (V < R 1 ), since there are no window shoppers. From (3) and (4) it can be easily shown that R 1 is decreasing in P N (the probability of meeting only shoppers) and R 2 is increasing in P N. The conclusion from Lazear is then that when P N is small there is a lot of opportunity for learning and thus prices start higher and fall more rapidly when unsold. For P N close to 1, prices tend to be more rigid as there is not so much per period learning. The same argument of learning also implies that given the number of buyers (i.e. given N and P N ), goods for which the prior distribution on V is more dispersed, i.e. large uncertainty on the part of the seller with respect to the buyer s valuation, have a higher initial price and larger second-period price decrease. However, the (unconditional) probability of a sale is lower for more dispersed priors. We apply Lazear s model to the housing market in the Netherlands. A house is a typical example of a good that is unique, prior valuation can be quite dispersed and list price changes occur frequently. For our paper the intuition provided by Lazear has two important implications. First, in liquid markets it pays to set a higher initial list price, which will be reduced relatively quickly as time proceeds because the intensity of learning is high. Second, when there is more uncertainty about the valuation of a house, list prices will be set higher, and it pays to take time to learn about the valuation of the house which results in a longer expected marketing time. 3. Related literature The model of Lazear discusses the pivotal role of learning and price revision in selling strategies when the buyer s valuation of products is unknown. The intuition is quite straightforward, although his model can be extended in a number of directions. One obvious extension is to allow for bargaining on the transaction price. In Lazear s model, customers are either willing to pay the asking price or not, i.e. the list price is also the reservation price. In practice, a seller jointly determines a list and reservation price and it is common practice that there is some room for bargaining. 2 Moreover, the number of customers inspecting the house will depend upon the list price. The search process of potential buyers 2 See Arnold (1999) for a game-theoretical equilibrium analysis on the dual role of list prices 1) influencing the arrival of customers and 2) marking the bargaining position of the seller. Arnold provides comparative statistics and thereby does not consider the opportunity of list price revisions and time on market. In fact, the bargaining process provides a lot of additional information since by bidding the customers reveal more information on their valuation of the house, than by just rejecting the asking price. If the house remains unsold for some time, this information provides valuable input for a revision of list and reservation prices. Thereby, the second period list price in Lazear s model becomes conditional on the information gathered in the first period. 4

8 consists of several steps. They first select houses with the characteristics they are interested in with prices they are willing and able to pay for. As house inspections are time consuming and costly, they cannot visit all houses that are for sale. They view list prices as a signal about the seller s reservation price and his patience, i.e. the motivation to sell quickly. At the same time, the list price marks the bargaining position of the seller being the first offer in the price negotiations. By implication, as a result of search and bargaining considerations there is a trade-off between the list price and the time it takes to sell a house. A relatively higher list price does imply a longer time to sell. This section reviews empirical literature that sheds light on the validity of Lazear s model and the implications of bargaining and search theory. Sass (1988) like Lazear does emphasize the relation between learning and price adjustments. He hypothesizes that the more learning occurs the more prices will be adjusted as the time on market proceeds. The better the information of the seller about the valuation of his house, the less room for learning and the more rigid the prices will be. In his empirical application based upon 415 house transactions in King County, Washington, brand new houses and recently sold houses where the seller has a good prior on the valuation of the house adjust their prices more slowly. At the same time very expensive houses where it is more difficult to establish the true value, thereby leaving more room for learning, show quicker price reductions. 3 Sass identifies the price reductions as the difference between the first list price and the ultimate transaction price. In that sense it confuses pricing strategies and bargaining power. There is no explicit role for list price revisions. In fact, there is not much empirical research on the role of list price revisions, mainly because information on price revisions is typically not available. An exception is the study by Knight (2002) who highlights the importance of list price revisions. Disregarding the information on list price revisions replicates the finding of a number of studies that the ultimate selling price is inversely related to the time on market. Including information on list price revisions reveals that patience is rewarded and ultimate selling prices are higher 4, but houses that were initially overpriced and had to resort to large price revisions ultimately sell at a lower price. This is evidence of the idea that strong list price reductions entail a stigma effect. In addition, the results show that the probability of list price revisions increases with the initial mark-up, is higher for vacant homes (where the seller faces higher costs in his search for a buyer), is lower for houses with unusual characteristics and increases with the time the house is on the market. 3 Note that the original interpretation by Sass is somewhat different, based on the idea that prices in very thin markets with a limited number of customers are adjusted more quickly. His reasoning is that each customer who decides not to buy the house provides the seller with a lot of information as it reduces the already small pool of potential buyers substantially. 4 Also Genesove and Mayer (1997, 2001) show a positive effect of patience on the time on market and the ultimate selling price. Their results are based on sellers that are either equity constrained (have a high loan-to-value ratio) or loss averse (do not want to sell their house for less than the original purchase price). An important implication is that this phenomenon explains the positive correlation between price increases and the number of trades in the housing market. 5

9 We focus on the interdependence of list price, time on market and list price revisions, and the role of market thinness. There are several studies that focus on the effect of the list price on time to sell. Anglin, Rutherford and Springer (2003), Rutherford, Springer and Yavas (2005), and Yavas and Yang (1995) find that an increase in the list price leads to an increase in time on market, but these papers do not consider and have no information on list price revisions. An important exception is the analysis by Herrin, Knight and Sirmans (2004) who do possess information on price revisions and confirm Lazear s predictions that prices are more rigid in thin markets when sellers have better information on the valuation of their house. Their empirical results focus on the number of price revisions and are based on sales of over 2000 single family dwellings in California, defining market thinness in terms of expensive houses and the atypicality index as suggested by Haurin (1988). We will test the same predictions, but then focusing on the time-on-market and time-to-price revisions and using a much larger dataset including single family dwellings as well as apartments. The relationship between time on market and Haurin s atypicality index is also investigated by Haurin (1988) himself and Krainer (1999). Both authors find for US data that houses with more unusual features need a longer marketing time, but do not consider the issue of list price revisions. 3.1 Hypotheses Our dataset is well suited for analyzing pricing behavior and time on market, i.e. the time to sale and the time to the first price revision, giving differences in pricing strategy, in house characteristics and in market liquidity. The considerations on learning, searching and bargaining give rise to the following hypothesis on the expected time on market and the expected time until a price revision: H1a: High DOP leads to a longer expected TOM H1b: High atypicality leads to a longer expected TOM H2a: High DOP leads to shorter time to price decrease H2b: High atypicality leads to longer time to price decrease Each of these hypotheses is partial, i.e. conditional on other circumstances being the same. Setting a higher list price in an attempt to learn about the market or to start with a strong bargaining position leads to a higher expected time on market, but also to a higher probability of price revisions when there is no sale and the seller learns about the valuation of buyers. For houses with atypical features there is much uncertainty about the value of the house and thus there is more room for learning which leads to a longer time on market and a longer time to price revisions (because there is much room for learning it is not in the interest of the seller to come with quick price revision). In addition, our results might shed light on the role of market thinness. We hypothesize that in more liquid markets (e.g. the 6

10 market for apartments with high turnover rates and thus a relatively high number of well-informed customers) there will be either a relatively quick sale or quick learning if the apartment is not sold and thus both the expected time on market and the expected time to a price revision will be shorter than in less liquid markets. 4. Data The data are collected on a weekly basis from the largest online multi-listing service in the Netherlands called Funda which contains more than 70% of the supply of houses listed by real estate agents. This is the market share of the largest Dutch association of real estate agents (NVM), which sponsors the Funda website. The same dataset, restricted to the Amsterdam region, is used in Gautier, Siegmann and Van Vuuren (2007). The start of our period of analysis is week 7 of 2004 (February) and the end of our period is week 6 of For every house we collect the address, zip code, the list price, the surface of the house and other features (like a garage) that may increase the value of the house. The list price of the house represents the ask price by the seller. There are no legal restrictions in the Netherlands concerning this price and the characteristics that are posted at the website. Even though this may be interpreted as a drawback of our analysis, there are no advantages for real-estate agents of giving inaccurate information on easily observable characteristics of the house because buyers always view the house before buying. Our dataset contains approximately 400,000 different objects, of which over 300,000 remain after we have dropped the left-hand censored observations of which we do not observe the first week of listing (See Table 1). We do not explicitly observe the sale of all the objects that are actually sold. Sometimes, our records indicate that a house is sold conditionally. This means that a buyer has agreed to buy the house, provided he can get the mortgage he needs or the house passes a technical inspection. In these cases, if the house actually disappears from the Funda website, we assume that it has been sold. Unfortunately, this information is not always provided. However, we do observe when the house or apartment is no longer listed. If the object is no longer listed and does not reappear on the Funda website we assume that the object has been sold. For objects that reappear within three weeks after they have disappeared, we assume that they have been removed by accident so we ignore this event. Objects that are relisted three or more weeks after they have disappeared are considered to be withdrawn and relisted by the seller on purpose. This is a known strategy used by real estate agents to trigger buyers interest in a house that has been on the market for a while without success. We distinguish between houses and apartments in our dataset because these are traded in separate markets. 7

11 This can clearly be seen from the histograms of initial list prices shown in Figure 1. There are also obvious differences in the time on market, as is clear from Figure Model specification To test our hypotheses we need to introduce the measures used in this paper to track the sale process of a house. First, we split our dataset into apartments and other houses, where apartments are generally traded in more liquid markets with higher turnover rates where more information is available. Following Anglin, Rutherford and Springer (2003), we assume that the list price is a signal to potential buyers. If the list price is higher than the expected list price, potential buyers are less likely to visit the house. The percentage difference between the expected list price and the actual list price is the degree of overpricing. The hypothesis is that houses with a lower DOP will sell faster than houses with a high DOP. Of course, DOP could also represent unobserved attributes of the property, but to the extent that these attributes are unobserved by the potential buyer, as well, the mechanism still works as described. To model a degree of overpricing, we specify a hedonic pricing model that captures the part of a house s price that can be explained by its characteristics. This is a common approach in the literature, see e.g. Rutherford, Springer and Yavas (2005) and Anglin, Rutherford and Springer (2003). In our case, we estimate p i = α + β ' X + ε, (5) J ( i) i i where p i is the log price of house i, X i is a vector with characteristics computed in deviation of average characteristics within the neighborhood and J(i) maps house i to neighborhood J. As such, α J(i) is the neighborhood effect and β gives the loading of the price on the individual characteristics. Neighborhoods are defined as the 4-digit postal code to which the house belongs. The part of the list price that cannot be explained consists of unobserved characteristics of the house, but it is also an indication of heterogeneity in seller s reservation prices, see Glower, Haurin and Hendershott (1998). We define the degree of overpricing as the difference between the list price p i and the fitted value pˆ i computed from equation (5): DOP = p pˆ (6) i i i To explain the time that the house is for sale, the time on market (TOM), we analyze durations with the use of hazard models. The hazard function specifies the probability of an event occurring at time t, given that the event has not occurred yet. One particular specification is that of a Cox-regression. The 8

12 Cox model is a semi-parametric model and does not require specification of the baseline hazard. Suppose that the hazard function of a particular house i is given by λ t, X ) = λ ( t) exp( ξ ' X ). ( i 0 i Here, λ ( ) is the baseline hazard. Now, if there is another house j, we can formulate the hazard ratio as 0 t λ( t, X λ( t, X i j ) ) = exp ξ [ '( X X )] i j, where X X ) is the difference between the characteristics of the ( i j two houses. The advantage of this approach is that we do not need to specify the baseline hazard λ ( ) in order to estimate coefficients ξ. Regression results often report hazard ratio exp(ξ), which we 0 t will explain below in Section 6. Haurin s model of atypicality offers an explanation for why houses with unusual attributes take longer to sell. There are fewer buyers who strongly prefer atypical houses and so it takes longer to match these few buyers in the population with the atypical houses that are for sale. Atypicality also captures the extent of dispersion in prior valuation by measuring the absolute deviation of a house s characteristics relative to those in the same neighborhood. It is more difficult to asses a buyer s valuation of a house in a neighborhood where it is the black swan than when it is an apartment in an apartment complex with similar characteristics. Using the estimated coefficients from the hedonic pricing equation (5), we construct the Haurin (1988) measure of atypicality. We compute the deviations of the characteristics of each house or apartment from the average characteristics in the area. Then, we multiply these characteristics with the marginal value of these features that are reported in Table 1. Formally, atypicality for property i is measured as: Atyp = β ' (7) i X i where X i is the absolute value of a vector with characteristics computed in deviation of average characteristics within the neighborhood and β gives the loading of the price on the individual characteristics estimated in equation (5). 6. Empirical results The results for the hedonic price regression as specified in Equation (5) are in Table 2. We run separate regressions for houses and apartments because these are traded in segmented markets. As could be expected, it shows that surface, lot size, and the presence of a garage are strongly significant determinants in the pricing of a house. Adding up the coefficients for surface and lot size we find that 9

13 a 10% increase in surface area increases the house price by 7.9%. This is a plausible value as the price of a house is predominantly based on the surface, but the elasticity of the price with respect to surface cannot be larger than one. The presence of a garage increases the list price by 6.1%. For apartments, the surface effect is of comparative value: an increase in surface of 10% bears an estimated 8.1% higher price and the presence of a garage increases the list price by 7.6%. The positive coefficient on the first week of listing indicates an upward sloping trend in asking prices, reflecting improving market conditions in the Dutch housing market. The R-squares for the regressions of both houses and apartments are high, 59 and 63%, respectively. Given the size of the dataset, this can be expected. This is in line with finding of previous studies. Anglin, Rutherford and Springer (2003) have an R-square of 88%, including area dummies. We explain 59 and 63% of the variance within the area. As mentioned before we use list prices in our study, so not the actual transaction prices. However, as is shown by Horowitz (1992), there is a very strong relationship between list prices and transaction prices, suggesting that our results would not change much if we used transaction prices. 6.1 Determinants of time on market Table 3 lists the results for the estimation of the Cox-regression for the time that a house is listed. This regression relates to hypotheses H1a and H1b derived in Section 3. The reported numbers are hazard ratio s and can be interpreted as the effect of an explanatory variable on the probability of sale. A hazard ratio larger than one indicates that the variable increases the probability of sale and a hazard ratio smaller than one that the variable reduces the probability of sale. With respect to the houses, we see that a higher list price reduces the probability of sale. The list price consists of two parts: one part that can be explained by the explanatory variables in our hedonic regression and the unexplained residual, called DOP. These two elements of the list price have a significantly different effect on the probability of sale. A 10% higher price that is justified by the characteristics of the house (for instance if it is bigger), reduces the probability of sale by 4.8%. If the 10% higher price is not related to the characteristics, we measure 10% overpricing and the probability of sale decreases by 7.3%. For apartments, the probability of sale reduces by 3.8% if the higher price is justified by the characteristics of the property and 6.6% if it is overpricing. High atypicality leads to a decrease in the hazard rate. The only variable that has a positive effect on the hazard rate is the week number of first listing, i.e., the duration of listings decreases on average over the sample period. The estimates for the apartments are comparable in sign and size, with the notable exception of atyp. Atypicality matters a lot more for houses than for apartments. Given that atypicality for apartments will foremost be influenced by the surface, this is no surprise. Apartments that are atypical in the sense that they are smaller or larger than the average apartment in the neighborhood, sell almost as quickly as the typical apartment. 10

14 The measurement of DOP and Atyp is based on incomplete information on housing characteristics and an imperfect hedonic price estimate. Therefore, these variables are inevitably obtained with some measurement error. This means that our Cox-regressions might reveal an underestimate of the true proportionate response of the hazard to a change in the regressor due to attenuation bias. So, the size and significance of the results that we find could even be higher if we could measure our variables of interest more accurately. 6.2 Explaining list price changes Table 4 shows results for a Cox regression where the time to the first price decrease is analyzed. This regression relates to hypotheses H2a and H2b derived in Section 3. As expected, overpricing increases the probability of a price decrease. A 10% higher degree of overpricing increases the probability of a decrease in the list price with 2.7% for a house and with 9.7% for an apartment. In contrast, a 10% higher list price that can be explained by observable attributes reduces the probability of a price decrease by 3.5% for houses and by 3.7% for apartments. An atypical house is less likely to see a drop in the list price, whereas atypicality is not relevant for apartments. Our interpretation for this striking difference is that apartments are atypical in the sense that they are larger or smaller than the average apartment in the neighborhood. This aspect can easily be priced, as our hedonic regression shows. Therefore, an atypical apartment is less likely to be mispriced than an atypical house. 6.3 Competing risks In the analysis reported in table 3, we have analyzed the time on market of a house without taking into account the price reductions that we observe for some of these houses. In the competing risks analysis we take a slightly different approach. We analyze the time until the first event that we observe for the object. This can be either the sale of the house or a reduction in the list price. The competing risk analysis is performed by estimating a multinomial logit model as suggested by Allison (1982). Strictly speaking, this is allowed when our data are intrinsically discrete, i.e. sales and price reductions only occur at the boundaries of our weekly intervals. Of course, the process of selling a house or reducing the list price is intrinsically continuous, but since our time intervals are relatively short and the instantaneous probability of selling the house or reducing the list price relatively low, the discrete approximation seems reasonable. Table 5 gives the results for the competing risks model where the hazard rates for time to sale and time to a price decrease are estimated simultaneously. These results confirm the finding of the single risk analysis reported in Tables 3 and 4. For both houses and apartments, a higher degree of overpricing results in a lower probability of sale and a higher probability of a price reduction. Atypicality is only relevant for houses and reduces the probability of sale and the probability of a price reduction. This confirms the hypothesis by Lazear that houses traded in thin markets are less likely to experience a 11

15 price reduction. The finding that more expensive houses (and apartments) are less likely to experience a price reduction also points in this direction. The Cox regression gives us results for the proportional hazard, but the competing risks approach the whole hazard function including the baseline hazard. This allows us to include duration dependence and compare hazard rates for houses and apartments. The positive coefficient for log(t) (ln(t)) and the negative coefficient for logt2 (ln2(t)) indicate that both the probability of sale and the probability of a price decrease are first increasing with time and then decreasing. For both houses and apartments, the estimated maximum probability of sale is reached at 5 weeks. The maximum probability of a price decrease is estimated to be at 24 weeks for a house and 19 weeks for an apartment. We also confirm the consensus view that the housing market improved over The variable First week of listing is significantly positive, indicating that houses that were listed later during the sample period, have a higher probability of being sold. 7. Summary and conclusions We exploit a unique large scale dataset for the Dutch housing market to learn about the relation between list prices and the duration until a next event, either a house sale or a price revision, and the role of market liquidity herein. The pricing strategy, i.e. the determination of a list price and the decision when to convert to a reduction of the list price revisions, is important as sellers have a direct interest in both the revenues from the sale and the duration of selling period. Our empirical analysis confirms a number of hypotheses which are formulated based upon the reading of the literature which states that learning about the market plays a pivotal role in price setting strategies. At the same time, the split of the dataset in apartments and houses provides information on the effect of liquidity. This is important because the theory states that in more liquid markets learning proceeds at a faster speed. The empirical estimates confirm that a high degree of overpricing leads to a longer time on market, and to a shorter time to list price decreases. This applies both to houses and apartments. However, for apartments, the degree of overpricing has a smaller effect on the expected time to sell and a larger effect on the time to a price revision. Houses and apartments with atypical features have a higher expected marketing time, but the effect for houses is much larger than for apartments. At the same time atypicality gives rise to a longer time to price decreases for houses but not for apartments. The effects of overpricing and atypicality are quite different for houses and apartments. For atypical houses it is more difficult for sellers to assess the value of the house, whereas atypicality matters less for apartments. Our interpretation of this finding is that for an apartment in our dataset the surface is 12

16 by far the most important variable in determining the price. An apartment is atypical when its surface differs from the average surface in the neighborhood. This feature is easily incorporated in the price. For overpricing a similar explanation can be given. Since it is relatively easy to determine the fair price of an apartment, it is also relatively easy to determine whether an apartment is really overpriced (in which case the price must be reduced) or whether there are some unobserved characteristics which means that the seller just waits until the right buyer come along. The size of the dataset and the fact that contrary to many other studies - we have information on list prices, and both time on market and time to price revisions gives us the opportunity to explore the role of liquidity in housing markets on the timing of house sales and selling strategies. All in all, the empirical results suggest that overpricing is part of a strategy to search for the opportunity of high revenues and to learn about the market, where learning occurs at a higher speed in liquid markets. The results thereby underscore the implications of the theory on learning in markets with uncertain demand. The results also indicate that spillovers from the macroeconomic environment to the housing market will first be visible in the time-on-market. Effects on the price of dwellings will materialize first for apartments and houses with attributes that are close to average. 13

17 References ALLISON, P.D. (1982), Discrete-Time Methods for the Analysis of Event Histories, Sociological Methodology 13, ANGLIN, P.M., R. RUTHERFORD AND T. M. SPRINGER (2003), The Trade-off Between the Selling Price of Residential Properties and Time-on-the-Market: The Impact of Price Setting, Journal of Real Estate Finance and Economics 26(1), GAUTIER, P., A.H. SIEGMANN AND A.P. VAN VUUREN (2007), The Effect of the Theo van Gogh Murder on House Prices in Amsterdam, CEPR discussion paper GENESOVE, D. AND C.J. MAYER (1997), Equity and Time to Sale in the Real Estate Market, American Economic Review 87(3), GENESOVE, D. AND C.J. MAYER (2001), Loss Aversion and Seller Behavior: Evidence from the Housing Market, Quarterly Journal of Economics 116, GLOWER, M., D.R. HAURIN AND P.H. HENDERSHOTT (1998), Selling Time and Selling Price: The Influence of Seller Motivation, Real Estate Economics 26(4), HAURIN, D.R. (1988), The Duration of Marketing Time of Residential Housing, AREUEA Journal 16(4), HERRIN, W.E., J. R. KNIGHT AND C.F. SIRMANS (2002), Price Cutting Behavior in Residential Markets, Journal of Housing Economics 13(3), pp HOROWITZ, J.L. (1992) The Role of the List Price in Housing Markets: Theory and an Econometric Model Journal of Applied Econometrics 7(2) KNIGHT, J.R. (2002), Listing Price, Time on Market and Ultimate Selling price: Causes and Effects of Listing Price Changes, Real Estate Economics 30(2), KRAINER, J. (1999), Real Estate Liquidity, RSBSF Economic Review no. 3, LAZEAR, E.P. (1986), Retail Pricing and Clearance Sales, American Economic Review 76(1), PRYCE, G. AND K. GIBB (2006), Submarket Dynamics of Time to Sale, Real Estate Economics 34(3), pp RUTHERFORD, R.C., T.M. SPRINGER AND A. YAVAS (2005) Conflicts between principals and agents: evidence from residential brokerage, Journal of Financial Economics 76, SASS, T.R. (1988), A Note on Optimal Price Cutting Behavior under Demand Uncertainty, Review of Economics and Statistics 70(2), TURNBULL, G.K., J. DOMBROW AND C.F. SIRMANS (2006), Big House, Little House: Relative Size and Value, Real Estate Economics 34(3), YAVAS, A. AND S. YANG (1995), The Strategic Role of Listing Price in Marketing Real Estate: Theory and Evidence, Real Estate Economics 23(2),

18 Appendix A: Tables and Figures Table 1: Descriptive statistics Houses Apartments Number of objects 277, ,422 Initial list price (1000 EUR) median mean Std. dev Time on Market (weeks) median mean Std. dev Number of objects with price decrease 54,109 20,102 Week of price decrease median mean Std. dev Number of objects (not left-hand censored) 216,685 92,862 Table 2: Hedonic price regression This table shows the results for the hedonic price regression as specified in Equation (5), separately for houses and apartments. All variables defined as difference to the area-mean. Standard deviations are between parentheses. Houses Apartments Dependent variable: Log(Initial list price) Individual characteristics First week of listing * * ( ) ( ) Surface (log) 0.62 * 0.81 * (0.0017) (0.0018) Garage * * ( ) (0.0025) Total area (log) 0.17 * (0.0009) R * indicates significance at the 1% level 15

19 Table 3: Time to sale This table shows the hazard ratios resulting from a Cox regression, relating the relative hazard to the individual characteristics of the house. Standard deviations are between parentheses. Houses Apartments Individual characteristics Predicted price (log) 0.52 * 0.62 * (0.0066) (0.013) DOP 0.27 * 0.34 * (0.0040) (0.0087) First week of listing * * ( ) ( ) Atypicality 0.41 * 0.84 * (0.0080) (0.025) χ 2 -test for joint significance * indicates significance at the 1% level Table 4: Time to first price decrease This table shows the results for the Cox regression, relating the relative hazard to the individual characteristics of the house. Standard deviations are between parentheses. Houses Apartments Individual characteristics Predicted price (log) 0.65 * 0.63 * (0.014) (0.024) DOP 1.27 * 1.97 * (0.031) (0.095) First week of listing * ( ) ( ) Atypicality 0.52 * 0.95 (0.017) (0.053) χ 2 -test for joint significance * indicates significance at the 1% level 16

20 Table 5: Competing risks This table shows the estimated parameters for the regression of the competing-risks model. The column labeled H-ratio reports hazard ratio s that can be compared to the numbers reported in Tables 3 and 4. Standard deviations are between parentheses. Houses Apartments Exit to sale coeff H-ratio coeff H-ratio Constant * * (0.012) (0.017) Predicted price (log) * * 0.61 (0.015) (0.024) DOP * * 0.30 (0.018) (0.030) First week of listing * * ( ) ( ) Atypicality * * 0.81 (0.022) (0.034) Logt 0.64 * 0.59 * (0.01) (0.015) logt * * (0.0026) (0.0037) Exit to price decrease Constant * * (0.058) (0.080) Predicted price (log) * * 0.61 (0.02) (0.039) DOP 0.16 * * 1.90 (0.026) (0.049) First week of listing * ( ) ( ) Atypicality * (0.030) (0.056) Logt 3.96 * 3.54 * (0.042) (0.060) logt * * (0.0076) (0.011) χ 2 -test for joint significance * indicates significance at the 1% level 17

21 Percent 15 Houses Apartments Initial list price Figure 1: Histogram Initial list price (1000 EUR) This Figure shows the histogram for initial list prices for houses and apartments 18

22 1.00 Kaplan-Meier survival estimates analysis time houses apartments Figure 2: Survival functions This Figure shows the survival functions for houses and apartments. For each week it shows the probability that an object is still on the market, separate for houses (upper curve) and apartments (lower curve). 19

23 Previous DNB Working Papers in 2008 No. 163 No. 164 No. 165 No. 166 No. 167 No. 168 No. 169 No. 170 No. 171 No. 172 No. 173 No. 174 No. 175 Carin van der Cruijsen and Sylvester Eijffinger, Actual versus perceived transparency: the case of the European Central Bank Jacob Bikker and Janko Gorter, Performance of the Dutch non-life insurance industry: competition, efficiency and focus Maarten van Rooij and Federica Teppa, Choice or No Choice: What Explains the Attractiveness of Default Options? Peter Wierts, How do Expenditure Rules affect Fiscal Behaviour? Jacob Bikker and Laura Spierdijk, How Banking Competition Changed over Time Willem Heeringa, Optimal life cycle investment with pay-as-you-go pension schemes: a portfolio approach Kerstin Bernoth, Andrew Hughes Hallett and John Lewis, Did fiscal policy makers know what they were doing? Reassessing fiscal policy with real-time data Alan S. Blinder, Michael Ehrmann. Marcel Fratzscher, Jakob de Haan and David-Jan Jansen, Central Bank Communication and Monetary Policy: A Survey of Theory and Evidence M. van Leuvensteijn, C. Kok Sørensen, J.A. Bikker and A.A.R.J.M. van Rixtel, Impact of bank competition on the interest rate pass-through in the euro area Stijn Claessens and Neeltje van Horen, Location Decisions of Foreign Banks and Institutional Competitive Advantage Peter Spreij, Enno Veerman and Peter Vlaar, Multivariate Feller conditions in term structure models: Why do(n t) we care? Iman van Lelyveld and Klaas Knot, Do financial conglomerates create or destroy value? Evidence for the EU Jan Willem van den End, Liquidity Stress-Tester: A macro model for stress-testing banks liquidity risk

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