Frequency of Transaction and House Price Modeling

This article examines the characteristics and price behavior of repeatedly transacted properties. Using data from four U.S. counties, we estimate hedonic price models of properties grouped by transaction frequency, and compare estimated standard deviations and estimated appreciation rates by group.For each of four counties studied, we find that estimated house price appreciation is systematically higher among properties that transact more frequently. One possible explanation for this result is that purchasers make property improvements that are not adequately reflected in the available data.We also find that estimated standard deviations of the disturbance term show a marked decrease as the frequency of transaction increases. Since frequently transacting properties are not found to be systematically more homogeneous than seldomly transacting properties, we do not attribute this to any increase in homogeneity for frequently transacting properties, but rather to the length of time elapsed between transactions of properties.The findings of this article suggest that repeat-sales price models may need to be adjusted to account for cross-sectional variation in transaction probabilities---that is, the selectivity of the subsample of properties that transacted (or transacted repeatedly) during any finite study period.     

The Dynamics of Location in Home Price

It is well established that house prices are dynamic. It is also axiomatic that location influences such selling prices, motivating our objective of incorporating spatial information in explaining the evolution of house prices over time. In this paper, we propose a rich class of spatio-temporal models under which each property is point referenced and its associated selling price modeled through a collection of temporally indexed spatial processes. Such modeling includes and extends all house price index models currently in the literature, and furthermore permits distinction between the effects of time and location. We study single family residential sales in two distinct submarkets of a metropolitan area and further categorize the data into single- and multiple-transaction observations. We find the spatial component is very important in explaining house price. Moreover, the relative homogeneity of homes within the submarket and the frequency with which homes sell affects the pattern of variation across space and time. Differences between single and repeat sale data are evident. The methodology is applicable to more general capital asset pricing when location is anticipated to be influential.     

Neighborhood Diversity and House-Price Appreciation

This study examines changes in house prices relative to the level of and change in percent racial/ethnic composition for certain counties in Tampa and Orlando, Florida. Repeat-sales transactions between 1971 and 1997 are used to create a constant quality price index for each city. The index for Tampa shows that the average annual house price appreciation was 5.89 percent over the period 1970 through 1997. The index for Orlando shows that the average annual house price appreciation was 5.25 percent over the 1970 through 1997 period. When the Tampa index model is expanded to account for race/ethnicity, household factors, and economic factors, the level of African American population has no significant effect on house-price appreciation; however, the change in percent African American has a negative effect. The level of percent Hispanic population has a positive effect, and the change in percent Hispanic has a positive effect. The expanded Orlando model shows that the level of percent African American population has no significant effect on price appreciation, while the change in percent African American has a negative effect. The level of Hispanic population has a positive effect, while the change in percent in Hispanic has a negative effect.     

Controlling for Quality in House Price Indices

Given the importance of house prices it is not surprising that house price indices are used for many purposes. One of the factors that differentiates these indices is the house price determinants (such as structural characteristics and neighborhood quality) that are accounted for—that is, held constant. Indices are usually generated from house price regressions. It is shown that, regardless of the desired level of accounting, it is necessary to control for all significant determinants of house prices in these regressions to obtain unbiased estimates of the growth in house prices. An empirical example shows that not controlling for neighborhood quality can lead to substantial biases in estimates of house price appreciation rates even if the index does not account for this factor.     

Repeat Sales Index for Thin Markets

The repeat sales model is commonly used to construct reliable house price indices in absence of individual characteristics of the real estate. Several adaptations of the original model by Bailey et al. (J Am Stat Assoc 58:933–942, 1963) are proposed in literature. They all have in common using a dummy variable approach for measuring price indices. In order to reduce the impact of transaction price noise on the estimates of price indices, Goetzmann (J Real Estate Finance Econ 5:5–53, 1992) used a random walk with drift process for the log price levels instead of the dummy variable approach. The model that is proposed in this article can be interpreted as a generalization of the Goetzmann methodology. We replace the random walk with drift model by a structural time series model, in particular by a local linear trend model in which both the level and the drift parameter can vary over time. An additional variable—the reciprocal of the time between sales—is included in the repeat sales model to deal with the effect of the time between sales on the estimated returns. This approach is robust can be applied in thin markets where relatively few selling prices are available. Contrary to the dummy variable approach, the structural time series model enables prediction of the price level based on preceding and subsequent information, implying that even for particular time periods where no observations are available an estimate of the price level can be provided. Conditional on the variance parameters, an estimate of the price level can be obtained by applying regression in the general linear model with a prior for the price level, generated by the local linear trend model. The variance parameters can be estimated by maximum likelihood. The model is applied to several subsets of selling prices in the Netherlands. Results are compared to standard repeat sales models, including the Goetzmann model.     

Transaction Frequency and Commercial Property

Metrics using repeat sale data assume that frequently and infrequently sold properties are similar in capital expenditures, maintenance and other characteristics. Value-added investors concentrate on repositioning properties which requires capital investment and managerial skills. Returns using repeat sales likely overstate appreciation by misattributing this investment. Present results show that frequently and infrequently traded properties represent different property populations. The first sale of a repeat transaction sells at a significant discount compared to single sale properties while the second sale transacts at a premium. The results suggest that repeat sale indices may overstate price appreciation and represent returns for a different, relatively small cohort of properties when compared to the large number of properties that transact only once during a specific time period.     

The Impact of Range Pricing on Marketing Time and Transaction Price: A Better Mousetrap for the Existing Home Market?

In various markets around the country, some real estate professionals are employing a new pricing strategy that involves marketing homes for sale with a price range rather than a single asking price. This strategy is often touted as a mechanism that will attract more potential buyers to look at a house and thus result in reduced marketing times for existing homes, with prices determined by competitive forces. The purpose of this study is to empirically examine whether houses using range pricing, often referred to as value range marketing, sell in the same amount of time and sell for similar prices as those marketed in the traditional manner. Two staged least squares with a correction for sample selection and Weibull duration models are used to test the hypotheses, employing a sample of 5,852 residential houses that were sold during the period January 1999 to December 2000. In contrast to claims of the strategy’s proponents, the results indicate that houses take longer to sell when using the range pricing strategy after controlling for physical characteristics and market conditions. Furthermore, there is no evidence that this strategy has any significant impact on transaction prices.     

The Dynamics of Gasoline Prices: Evidence from Daily French Micro Data

Using millions of individual gasoline prices collected at a daily frequency, we examine the speed at which market prices of refined oil are transmitted to retail gasoline prices in France. For that, we estimate a reduced‐form model of state‐dependent pricing where thresholds triggering price changes are allowed to vary over time and depend on the duration since the last price change. We find that the degree of pass‐through of wholesale prices to retail gasoline prices is on average 0.77 for diesel and 0.67 for petrol and depend on local market characteristics. The duration for a shock to be fully transmitted into prices is about 10 days. There is no significant asymmetry in the transmission of wholesale price to retail prices. Finally, the duration since the last price change has a significant effect on thresholds triggering price changes but a large variance of idiosyncratic shocks on thresholds is also crucial to replicate the size distribution of price changes.     

Analysis of Spatial Autocorrelation in House Prices

This article examines spatial autocorrelation in transaction prices of single-family properties in Dallas, Texas. The empirical analysis is conducted using a semilog hedonic house price equation and a spherical autocorrelation function with data for over 5000 transactions of homes sold between 1991:4 and 1993:1. Properties are geocoded and assigned to separate housing submarkets within metropolitan Dallas. Hedonic and spherical autocorrelation parameters are estimated separately for each submarket using estimated generalized least squares (EGLS). We find strong evidence of spatial autocorrelation in transaction prices within submarkets. Results for spatially autocorrelated residuals are mixed. In four of eight submarkets, there is evidence of spatial autocorrelation in the hedonic residuals for single-family properties located within a 1200 meter radius. In two submarkets, the hedonic residuals are spatially autocorrelated throughout the submarket, while the hedonic residuals are spatially uncorrelated in the remaining two submarkets. Finally, we compare OLS and kriged EGLS predicted values for properties sold during 1993:1. Kriged EGLS predictions are more accurate than OLS in six of eight submarkets, while OLS has smaller prediction errors in submarkets where the residuals are spatially uncorrelated and the estimated semivariogram has a large variance.     

A New Spin on the Jumbo/Conforming Loan Rate Differential

This article uses house-price transaction data to estimate volatility in house prices. The volatility parameter is an input into a mortgage-pricing model that is used to simulate the contract interest rate that balances the mortgage contract. By segmenting the house-price transaction into high- and low-valued homes, we are able to estimate a theoretical jumbo/conforming loan rate differential. Simulation results demonstrate that the differences in volatility between high- and low-priced homes can produce a contract loan rate differential, holding all else constant. The article also presents a discussion of the problems inherent to estimating volatilities form assets with infrequent trades and long holding periods.