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Offer price, transaction price and time-on-market

Offer price, transaction price and TOM

Kicki Bjo¨rklund John Mattson Fastighets AB, Lidingo¨, Sweden, and

415

John Alex Dadzie and Mats Wilhelmsson Building and Real Estate Economics, Royal Institute of Technology, Stockholm, Sweden Abstract Purpose – The purpose of this paper is to investigate whether or not the offer price affects the transaction price and the number of days the property is on the market. Specifically, is it possible for the broker to use the offer price as an instrument for obtaining a higher transaction price? Design/methodology/approach – To test the hypothesis the general hedonic model is used, where the deviation of the transaction price and expected price from the offer price is a function of time on the market. Findings – The results indicate that a high offer price is more likely to result in a high ratio of transaction price to expected price compared to a low offer price. Research limitations/implications – However, the overall conclusion is affected by the state of the market, that is, whether the market is static, rising or falling. Practical implications – The best selling strategy in a rising market seems to be set a high offer price compared to the expected sale price. Originality/value – The main contribution is that the paper not only analyzes the relationship between offer and transaction price, but also its relationship to expected price. It also tests for the existence of spatial autocorrelation, which is unique in this type of study. Keywords Real estate, Purchase agreements, Pricing policy Paper type Research paper

1. Introduction The relationship between offer prices, transaction price and time-on-the-market (TOM) is not an unexplored research area. A number of papers have been published over the past ten years. Asabere et al., (1993) investigate the relationship between price and optimal TOM. Their results indicate that overpricing increases the optimal TOM. Kalra and Chan (1994) analyze the impact of macroeconomic variables on TOM. High price concession decreases the TOM, high total employment decreases the TOM and high mortgage rates increase the TOM. Jud et al., (1996) analyze the relationship between TOM and brokerage firm and marketing efforts. Their conclusions are that the probability of sale increases with TOM, but individual broker and firm characteristics are not statistically significant. Papers that are more recent specifically Knight (2002) and Angelin et al., (2003), both analyze the impact of the initial price setting. In this paper, we investigate whether or not the offer price affects the transaction price and the number of days the property is on the market. We utilize a slightly different approach from the papers mentioned above. Given that our main objective is not to explain why the TOM may or may not increase, we are particularly interested in the price ratio of offer price and the actual transaction price. Specifically, is it possible

Property Management Vol. 24 No. 4, 2006 pp. 415-426 q Emerald Group Publishing Limited 0263-7472 DOI 10.1108/02637470610671631

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for the broker to use the offer price as an instrument for obtaining a higher transaction price? Our main contributions are, firstly, that we are not only analyzing the relationship between offer and transaction price, but also the relationship to expected price (estimated with mass appraisal method). Secondly, we test for the existence of spatial autocorrelation, something that has not been done before in this type of study. Thirdly, we perform a rigorous statistical test for sample selection bias. The paper is organized as follows: in Section 2, we present the theoretical and empirical model. In Section 3, we present the empirical results and in Section 4, we summarize and conclude the paper. 2. A short literature review Much research has been done on offer price, transaction price and TOM. This research provides different perspectives and mixed research findings, mainly on: . the relationship of TOM and price concession ratio; . TOM and sale price; and . search theory and TOM. Our research is in the same direction as the first two, but unlike previous studies that sought to explain the duration of TOM, we are particularly interested in how TOM relates to, or affects offer price ratio and actual transaction price. We specifically also explore the possibility that a broker might use an offer price to influence the transaction price during the time that a property for sale remains on the market. Miller (1978) has shown that it is not apparent that sellers (brokers) benefit from longer marketing periods in terms of achieving greater actual selling price. He used 91 observations of transaction prices for single family homes in Columbus, Ohio in the later part of 1976. He categorized them into full sample and subgroups of low, mid and high price range. Running a regression for only the full sample resulted in a significant coefficient on TOM. If we interpret the results literally, the average property in the sample increased its expected selling price by $55 for each day on the market. He demonstrated a positive inflation bias of approximately 37 percent. However, the coefficients for TOM in the subgroups of price ranges were not significant, precluding making any substantial conclusions. Larsen and Park (1989) used a model to demonstrate that the percentage commission rate on a property’s selling price has an ambiguous effect on TOM. Using data on 433 single family home transactions from Lancaster County, in the city of Lincoln in Nebraska for the first nine months of 1986, they found that property owners (through their brokers) who employ the strategy of initially pricing a property attractively low in order to sell quickly may be able to recover some of the upfront price concession. In essence, the longer a property remains on the market, all other factors being equal, the greater is the concession from price. Jud et al., (1996), explore the effect of the characteristics of brokerage firms on the time required to sell a house. Using a duration model and a data set of 2,285 housing transactions, they established that no particular broker or agent is able to market a home faster than any other. Their results also indicated that offer price, changes in offer price and house typicality are important determinants of TOM, and assert that higher offer prices are associated with longer TOM; thus reducing the offer price shortens TOM.

Asabere and Huffman (1993) examined the relationship between offer price concessions, TOM, and the actual sale price of homes and concluded that overpricing can lead to real discounts on sale price. They also found that the longer the TOM, ceteris paribus, the higher the probability that the selling price will be relatively superior. They utilized data on 337 residential sales over the time period from December 1986 to June 1990 from multiple listing services covering the Pennsylvania counties of Philadelphia, Montgomery and Chester. They employed the standard hedonic equation for a regression analysis, controlling for property characteristics, economic factors, price concession, and TOM. They established in their results that, at the 99 percent confidence level, every price concession point of 0.01 is associated with a 0.89 percent discount in actual sales price. Also, at the 95 percent confidence level, an increase of 1 percent in TOM would lead to a 6 percent increase in the actual sale price of a house. Asabere et al., (1993) investigated the relationship between price and optimal TOM. Their results indicate that both overpricing and underpricing result in sub-optimal sales prices. By minimizing the TOM subject to a number of constraints (such as mortgage rate and transaction price), they estimated the optimal TOM for each property. This showed that the optimal TOM is lower than the actual time spent. In the next step, they used the optimal TOM as the dependent variable explained by potential mispricing, which is the difference between offer price and expected price. Their result indicates that overpricing increases the optimal TOM. Overpricing by 1 percent produces an excess time of 0.4 percent beyond optimal TOM. Kalra and Chan (1994) analyzed the impact of macroeconomic variables on TOM. They used TOM as the dependent variable and mortgage rate, employment level and price concession as independent variables. High price concessions decrease the TOM, high total employment decreases the TOM and high mortgage rate increases the TOM, all as expected. They use a Tobin model because their data are censored, that is, in their data they also include properties without TOM (no sale). Jud et al., (1996) analyzed the relationship between TOM and brokerage firm and marketing efforts. Their conclusion is (not surprisingly) that the probability of sale increases with TOM, but (surprisingly) that individual broker and firm characteristics are not statistically significant. Knight (2002) focused his paper on causes and effects of listing price changes. The result indicates that mispricing is costly both in money and in time. Houses with large listing price changes have both a longer TOM and sell at lower prices. Angelin et al., (2003) presents a model analyzing the relationship between list price, transaction price, and TOM. They especially focus on the impact of price setting. The results imply that increases in list price increase TOM but they also show that this effect is magnified for houses with a low variance in list price. 3. Theoretical and empirical model To test our hypothesis we use the general hedonic model. The general hedonic model is a commonly used model in which the dependent variable is transaction price (Pt) and the independent variables (Xt) are housing attributes, neighbourhood indicators and (Dt) time indicators if the sample is a combination of cross-section and time series. The simple hedonic equation has the form: P t ¼ at þ bt X t þ ct Dt þ et

ð1Þ

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Usually the functional form is nonlinear but the equation is linear in parameters. If the hedonic equation is in a logarithmic form, all coefficients in the estimate will be elasticities. The estimated parameters (bt) can be interpreted as willingness to pay for the attribute in question (Rosen, 1974). See Wilhelmsson (2000) for an example of an empirical hedonic analysis. Though we have tried to account for the spatial dimension by including submarket indicators and distance to central business district (CBD), we are inclined to have some spatial autocorrelation problems. Spatial dependency is one form of spatial effect that affects the accuracy of ordinary least square (OLS) estimates of the hedonic price equation. If there is spatial dependency in the data, OLS estimates will be unbiased but inefficient and inconsistent (Dubin, 1998). Furthermore, the estimate of the variance will be biased, which makes statistical inference difficult or impossible (Anselin, 1988). The use of spatial econometrics can remedy the problem of spatial autocorrelation. However, the choice of spatial structure does affect the interpretation of parameters for variables correlated with it, i.e. a sort of multicollinearity problem. Hence, uncritical use of spatial econometrics may induce problems when interpreting individual parameters (Wilhelmsson, 2002). Moran’s I is one of many tests that measure the spatial correlation in the residuals of a regression model. If Moran’s I is larger than the critical value, we reject the hypothesis of no spatial correlation (Anselin, 1988). Moran’s I takes the form: I¼

e0 We e0 e

ð2Þ

where e is the vector of OLS residuals and W is the row standardized spatial weight matrix of the size nxn. The critical value is 1.96. If the test statistic is larger than the critical value, we have to reject the null hypothesis of no spatial autocorrelation. The inverse square distance between all properties defines the spatial weight matrix. We test the hypothesis that the deviation of the transaction price from the offer price is a function of the TOM (measured in number of days). We base the simple model on equation (1). Equation (3) indicates that the offer price can also been seen as a function of its attributes and the date of offer (Do): P o ¼ ao þ b o X o þ c o D o þ e o

ð3Þ

The difference between equations (1) and (3) is a function of the differences between the date of the offer and the date of the transaction, if we are willing to assume that all other exploratory variables are equal and thereby the difference equals zero. This gives us equation (4): P t 2 P o ¼ ðat 2 ao Þ þ ðct 2 co ÞðDt 2 Do Þ þ ðet 2 eo Þ

ð4Þ

We interpret (Dt 2 Do) to be the TOM. To estimate this relationship we use equation (5), which is exactly the same equation that Asabere and Huffman (1993) adopted. The transaction price is not only a function of its attributes, but is also a function of the TOM and the difference between offer price and transaction price. We improve the model by estimating equation (6). n X PO 2 PT bj X j þ 1 ð5Þ þ b2 ðTOMÞ þ lnðP t Þ ¼ ln b0 þ b1 PT j¼3

n X PO 2 PE lnðP t Þ ¼ ln b0 þ b1 bj X j þ 1 þ b2 ðTOMÞ þ PE j¼3

ð6Þ

where: Po – offer price; PT – transaction price; PE – expected price; Xj – exploratory variables; TOM; Greek letters parameters to be estimated. Instead of only analyzing the difference between offer price and transaction price, we are analyzing the difference between expected price (market value) and offer price. The reason for this is that we have a problem with endogeneity in equation (5), as the transaction price is both a dependent variable and an explanatory variable. It is more intuitive that the transaction price is a function of offer price and expected price, because the offer price has to be set in relation to the expected price the day the house is listed on the market. We estimate the expected price (market value) using a mass appraisal model on a much larger dataset, including all transactions in the county of Stockholm. 4. Empirical analysis 4.1 The data We only use the full sample to estimate expected price for all transactions in the sub-sample. The full sample includes all transactions in the county of Stockholm – 12,168 observations for the period 1999-2001. We have information on price, living area, other accommodation, lot size, quality, transaction date and age, sub-market dummies and distance to CBD. For the sub-sample of 704 observations, we have the same information as in the full sample but additional information, such as offer price and offer date collected from the internet listing system. We analyzed the market for single-family houses in the county of Stockholm for the period 1999-2002. There is of course a time lag between offer data (sub-sample) and transaction data (full sample) that is illustrated in the period: . offer data (December, 1999-May, 2001); and . transaction data (February, 2000-February, 2002). We estimate equations (5) and (6) using a sub-sample of around 5 percent, which contains of transactions marketed on internet. 4.2 Descriptive analysis Table I presents descriptive statistics. Comparing the full sample with the sub-sample, we find that there are some differences in the average value. It appears that the full sample includes properties with bigger lot sizes, located more peripherally relative to the CBD. This could be the reason why the expected price appears to be so low. We should mention that the sub-sample is predominantly in Stockholm, which can partly explain the fact that the objects listed in the internet system at the start of the system were high-quality objects. Table II shows the differences in transaction price and offer price for the different sub-markets. Here, we observe large differences in the figures. In some sub-markets, there is a negative difference (5 out of 56), which means that the property sold at a price lower than the offer price. It can be observed that the sub-markets in which we have large positive differences are attractive high-demand neighborhoods and the five sub-markets with negative difference are located in low-demand neighborhoods that are not very attractive.


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Table I. Descriptive statistics

Average Actual price (SEK 000) Offer price (SEK 000) Expected price (SEK 000) Price difference 1 Price difference 3 Living area (m2) Lot size (m2) Age Month Other area Distance to CBD (m) Quality (index) Time on market (days) Number of observation

2,791.5 2,610.6 2,616.0 20.1021 20.1581 129.1 1,039.8 44.29 13.62 54.75 12,132 28.3 110.02 704

Sub-sample Standard deviation 1,391.3 1,367.1 1,106.9 0.39 0.61 45.9 1,468.3 20.45 5.74 39.54 6,680 5.3 71.12

Average

Full sample Standard deviation

2,607.6 – –

1,844.0 – –

130.85 1,477.90 40.63 23.71 50.24 15,016 28.27 – 12,168

49.83 8,844.30 21.08 9.08 42.98 8,518 5.75 –

4.3 Econometric analysis The estimation of the hedonic price equation uses seven attributes to explain price variation. The first three attributes measure living areas, other indoor areas, and outdoor lot size in square meters. The fourth attribute in the model is age, measured as the effective number of years. The fifth attribute is indoor quality. This attribute, used for tax assessment purposes, is constructed from data provided by the owner of the property. It is an index based on information about construction material and amenities. The quality index is a composite of 25 questions about quality where some of the questions can give up to three index units. The sixth attribute measures the distance in meters from the object to the CBD. The seventh attribute is a continuous time variable (month). In addition to these seven attributes, the hedonic model includes 89 dummy variables for the sub-markets. The sub-markets are the municipality and administrative parish. Of course, this may not be consistent with the different sub-markets in reality, but if this is the case, the spatial autoregressive model should correct for it. The mass appraisal model uses all observations in the full sample and is marked with asterisks. In Table III, four models equations (1) and (4)-(6) use the sub-sample. The first equation (1) is an OLS model without the price concession and TOM variables. The price concession models are shown in the last four columns. The parameters for the housing attributes change very modestly across the different models and specifications. Most of them are of reasonable magnitude and statistically different from zero. However, there is a difference between the result using the full sample and the sub-sample, which could indicate a sample selection bias. In the second column, a sub-sample dummy has been included in the estimation of equation (1) with the full sample. The estimated parameter is positive and statistically significant, that is, the sample observations are on average more expensive (4 percent higher). This could indicate that marketing through the internet is a more effective marketing strategy compared to traditional marketing.

Sub-market ASPUDDEN BAGARMOSSEN BANDHAGEN BROMMA KYRKA BROMSTEN ¨ LLSTA BA ENEBY ˚ RD ENSKEDE GA ENSKEDEDALEN ¨ LTET ENSKEDEFA FAGERSJO¨ FLYSTA GAMLA ENSKEDE ¨ NGEN GUBBA ¨ NGEN HERRA ¨ GERSTEN HA ¨ GERSTENSA ˚ SEN HA ¨ SSELBY VILLASTAD HA HO¨GLANDET ¨ NGEN HO¨KARA ¨ LVESTA KA LISEBERG ˚ NGBRO LA ˚ NGSJO¨ LA ¨ LL MARIEHA ¨ LARHO¨JDEN MA NOCKEBY NOCKEBYHOV ¨ NGBY NORRA A ¨ LSTA NA OLOVSLUND ˚ CKSTA RA ¨ CKS GA ˚ RD SKARPNA ¨ NDAL SKO ¨ TTEN SMEDSLA SOLBERGA SOLHEM STORA ESSINGEN STORA MOSSEN STUREBY SUNDBY SVEDMYRA ¨ TRA SA ¨ DRA A ¨ NGBY SO TALLKROGEN ULVSUNDA VINSTA ˚ RBERG VA ¨ STBERGA VA ˚ KESHOV A ˚ LSTEN A ¨ LVSJO¨ A ¨ PPELVIKEN A O¨RBY O¨RBY SLOTT TOTALT

Differences in transaction and offer price 0.094 0.142 0.074 0.126 0.061 0.108 0.179 0.293 20.056 0.065 0.048 0.022 0.191 0.021 0.114 0.220 0.105 0.064 0.074 20.048 0.092 20.013 0.175 0.133 0.129 0.026 0.217 0.172 0.036 20.062 0.217 0.145 0.152 0.139 0.356 0.220 0.073 0.110 0.169 0.152 0.072 20.033 0.088 0.046 0.093 0.170 0.075 0.088 0.031 0.146 0.141 0.098 0.186 0.131 0.217 0.116


Table II. Differences in transaction and offer price in different sub-markets in the municipality of Stockholm

Table III. Estimation of equations (1) and (4)-(6). The logarithm of the transaction price is the dependent variable – – 0.6508 – 12,168

– 0.6477 – 12,168

–

–

–

0.000294 (1.86) 0.010374 (34.60) 0.000393 (5.70) 0.004212 (66.26) 0.000002 (7.73) 0.011185 (21.10) 20.009995 (210.73) 0.044514 (3.78)

13.525246 (398.78)

0.000282 (1.79) 0.010433 (34.82) 0.000393 (5.70) 0.004213 (66.22) 0.000002 (7.72) 0.011211 (21.13) 20.010025 (210.75) –

13.530831 (399.11)

Note: Full sample model (mass appraisal model)

a

TOM TOM squared Adj. R 2 Morans’ I n

Age Month Other area Living area Lot size Quality Distance CBD Sub-sample Price concession

Constant

Equation, (1)a Equation (4)

Equation (5)

Equation (6)

– 0.6221 3.9163 704

– 0.0086 2.6500 704

0.7208 4.0681 704

– 0.7006 2.6985 704

–

14.100736 (91.42) 298,656.39 (5.69) 14.183528 (106.53) 13.836206 (99.67) 20.00443 20.000204 (20.57) – (20.31) 20.000717 (21.04) 0.004327 (1.59) – 0.011853 (4.47) 0.008320 (3.03) 0.000488 (1.58) – 0.000431 (1.62) 0.000588 (2.14) 0.003785 (12.20) – 0.003688 (13.76) 0.004167 (14.99) 0.000030 (3.90) – 0.000031 (4.75) 0.000015 (2.23) 0.004690 (2.05) – 0.003947 (2.01) 0.006929 (3.39) 20.000022 20.000024 (23.79) – (24.90) 20.000015 (22.84) – – – – 20.229853 – – (214.16) 0.342637 (12.05) 21,070.40 20.000441 – (22.67) (22.89) 20.000653 (24.16)

Equation (1)

422

Equation (1)a

704

0.000810 (2.30) 20.000003 (24.62) 0.7101

0.338508 (12.10)

13.768935 (100.23) 20.000686 (21.01) 0.007763 (2.87) 0.000594 (2.20) 0.004049 (14.74) 0.000016 (2.28) 0.006490 (3.22) 20.000015 (22.85) –

Equation (6)

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Equation (4) is a very simple model where the price difference between transaction and offer price is a function of TOM. As can be seen, the model can only partially and modestly explain the variation around the difference. However, the parameter for TOM is statistically significantly different from zero. If TOM increases by one day, we expect that the price difference will decrease by 1,000 SEK. In equation (5), we define the price concession as the difference between offer price and transaction price. This means that every increase in price concession of one percent is associated with a 0.23 percent discount in actual transaction price. Equation (5) is exactly the same model as in Asabere and Huffman (1993). However, their results indicate a 0.9 percent discount in transaction price. The parameter for TOM is negative, indicating that the price will decrease if the TOM increases. Interpreted literally, if TOM increases by one day, we expect the transaction price to be reduced by around 0.04 percent or if TOM increases by one month, the actual price will be around 1.2 percent lower, everything else being equal. This parameter is highly statistically significant. The magnitude of the result is the same as in Asabere and Huffman (1993), but the sign is the opposite. That is, their conclusion is that an increase in TOM would lead to an increase in actual sale price of the property. One reason could be that we are analyzing transactions in a booming housing market and if the property is on the market a long time, it is an indication of a “lemon”. Another reason could be that the relationship between price and TOM is not linear. Later we test a nonlinear relationship between price and TOM. Since there is a problem with endogeneity in equation (5), we have estimated equation (6). Instead of using transaction price, we use expected price. We estimated the expected price from a mass appraisal model. However, we are not only using the sub-sample, but also the full sample of 12,000 observations. If the difference between offer price and expected price increases by one percentage point, as in equation (6), the property will sell for a premium of 0.16 percent. That is, setting an offer price higher than the expected price will lead to a higher transaction price. In the Asabere and Huffman study, the actual sale price is lower than the offer price and they characterize the market as a buyer’s market. As we are analyzing a booming market, it is more of a seller’s market. If we include TOM squared in the model we get some interesting results. The first parameter for TOM is positive and the second negative. That indicates that the price will increase as TOM increases but only up to a point, but after that price will decrease. As can be observed in Figure 1 below, price increases for up to 270 days (nine months) and decreases after that. The maximum effect on price is at 150 days (five months). If we use our results in a numerical example we can draw the following conclusion: if the expected price is 2,000,000 SEK, an offer price of 1,800,000 SEK (10 percent lower than the expected price) will lead to a transaction price 3 percent lower than the expected price. On the other hand, an offer price of 2,200,000 SEK leads to a 3 percent higher transaction price than the expected price, everything else equal. We show the relationship between transaction price and price concession in Figure 2. There is a very small spatial correlation in the residuals, which is a little surprising. The spatial autoregressive model indicates only modest differences in the parameters. We present the results of that model in the Appendix.


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Figure 1. The relationship between price and time on market

Figure 2. Price concession and transaction price, percent

5. Conclusion The correlation between TOM and offer and transaction price have been analyzed in a number of studies. In the present study, we introduce the concept of expected price. Our main objective is to investigate whether the offer price has any impact on the relationship between transaction price and expected price. Our main result is that a high offer price is more likely to result in a high ratio of transaction price to expected price compared to a low offer price. We show that if the offer price is equal to or higher than the expected price, the transaction price will be higher. However, there is a trade-off between the TOM and the transaction price. A high

offer price will lead to longer sale process compared to a low offer price, but the transaction price will be higher. That is, the best selling strategy seems to be to set a high offer price compared to the expected price. It is clear that the relationship is affected by the state of the market, that is, whether the market is static, rising or falling. However, as we analyze a booming market, the results are only valid for a housing market with rising house prices. To some extent, our results contradict earlier empirical results. One obvious reason could be that our estimates of expected price are systematically overestimated or underestimated. Low-quality houses could be systematically overestimated (even if we control for housing quality), but on the other hand, high-quality houses could also be underestimated. We see no reason why this would affect the results that we present here, as, on average, the effect of under- or overestimation of the expected price should cancel out. There is no reason to believe that there is only overestimation of the expected price for low-quality houses or that these overestimates are higher (in absolute value) than the underestimation of the expected price for high-quality houses. However, this matter merits further empirical investigations. References Angelin, P.M., Rutherford, R. and Springer, T.M. (2003), “The trade-off between the selling price of residential properties and the time-on-the-market: the impact of price setting”, Journal of Real Estate Finance & Economics, Vol. 26 No. 1, pp. 95-111. Anselin, L. (1988), Spatial Econometrics: Method and Models, Kluwer Academic Publishers, Dordrecht. Asabere, P.K. and Huffman, F.E. (1993), “Price concession, time on the market, and the actual sale price of homes”, Journal of Real Estate Finance & Economics, Vol. 6, pp. 167-74. Asabere, P.K., Huffman, F.E. and Mehdian, S. (1993), “Mispricing and optimal time on the market”, Journal of Real Estate Research, Vol. 8 No. 1, pp. 149-56. Dubin, R.A. (1998), “Spatial autocorrelation: a primer”, Journal of Housing Economics, Vol. 7, pp. 304-27. Jud, D.G., Seaks, T.G. and Winkler, D.T. (1996), “Time on the market: the impact of residential brokerage”, Journal of Real Estate Research, Vol. 12 No. 3, pp. 447-58. Kalra, R. and Chan, K.C. (1994), “Censored sample bias, macroeconomic factors, and time on market of residential housing”, Journal of Real Estate Research, Vol. 9 No. 2, pp. 253-62. Knight, J.R. (2002), “Listing price, time on market, and ultimate selling price: causes and effects of listing price changes”, Real Estate Economics, Vol. 30 No. 2, pp. 213-37. Larsen, J.E. and Park, W. (1989), “Non-uniform percentage brokerage commission and real estate market performance”, AREAEA Journal, Vol. 17 No. 4, pp. 423-38. Miller, N.G. (1978), “Time on the market and selling price”, AREUREA Journal, Vol. 6 No. 2, pp. 164-74. Rosen, S. (1974), “Hedonic prices and implicit markets: product differentiation in pure competition”, Journal of Political Economy, Vol. 82, pp. 34-55. Wilhelmsson, M. (2000), “The impact of traffic noise on the values of single-family houses”, Journal of Environmental Planning & Management, Vol. 43, pp. 799-815. Wilhelmsson, M. (2002), “Spatial econometrics in real estate economics”, Housing, Theory and Society, Vol. 19, pp. 92-101.


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Appendix

Equation (6)

426

Table AI. The spatial autoregressive model

Constant Age Month Other area Living area Lot size Quality Distance CBD Price concession TOM r Adj. R 2 n

13.081240 (25.15) 20.000036 (2 0.06) 0.007359 (3.17) 0.000222 (0.95) 0.002327 (9.38) 0.000020 (3.39) 0.001398 (0.81) 20.000017 (2 5.20) 0.320815 (2 18.80) 20.000534 (2 4.00) 0.083164 (2.61) 0.7565 704

Corresponding author Mats Wilhelmsson can be contacted at: [email protected]

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