Determinants of House Prices: A Quantile Regression Approach

OLS regression has typically been used in housing research to determine the relationship of a particular housing characteristic with selling price. Results differ across studies, not only in terms of size of OLS coefficients and statistical significance, but sometimes in direction of effect. This study suggests that some of the observed variation in the estimated prices of housing characteristics may reflect the fact that characteristics are not priced the same across a given distribution of house prices. To examine this issue, this study uses quantile regression, with and without accounting for spatial autocorrecation, to identify the coefficients of a large set of diverse variables across different quantiles. The results show that purchasers of higher-priced homes value certain housing characteristics such as square footage and the number of bathrooms differently from buyers of lower-priced homes. Other variables such as age are also shown to vary across the distribution of house prices.

A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value.     

Predicting House Prices Using Multiple Listings Data

It is often necessary to accurately predict the price of a house between sales. One method of predicting house values is to use data on the characteristics of the area's housing stock to estimate a hedonic regression, using ordinary least squares (OLS) as the statistical technique. The coefficients of this regression are then used to produce the predicted house prices. However, this procedure ignores a potentially large source of information regarding house prices—the correlations existing between the prices of neighboring houses. The purpose of this article is to show how these correlations can be incorporated when estimating regression coefficients and when predicting house prices. The practical difficulties inherent in using a technique called kriging to predict house prices are discussed. The article concludes with an example of the procedure using multiple listings data from Baltimore.     

The Benefits of Visibility Improvement: New Evidence from the Los Angeles Metropolitan Area

This article examines the impact of a specific aspect of air quality—visibility, or the ability to clearly see distant objects—on housing values. Our analysis is based on a data set constructed by matching residential housing sales data from the Los Angeles Metropolitan Area for the period 1980 through 1995 with visibility and other air pollution data and other characteristics. We find that visibility differences are capitalized into housing values, producing a measurable hedonic price gradient. The time-series design facilitates an estimate of the demand for visibility that we use to calculate the benefits of changes in visual range.     

Price Indices Based on the Hedonic Repeat-Sales Method: Application to the Housing Market

Shiller (1993) proposes the hedonic repeated-measures (HRM) approach to measuring constant quality price indices for heterogeneous assets such as some bonds and real estate. We derive a mathematical relationship between the coefficients of the HRM model and those from the standard repeat-sales model, and we demonstrate how hedonic characteristics should be chosen for inclusion in the HRM model. Empirical estimates using Fairfax, Virginia, housing transactions data show that the HRM price index evaluated at the mean of the hedonic variable is virtually identical to the standard repeat sales index, just as predicted by our mathematical relationship. But the HRM allows estimation of different price paths for heterogeneous assets. We demonstrate that use of assessed value as the only hedonic characteristic allows parsimonious HRM estimates.     

Spatial Autocorrelations and Urban Housing Market Segmentation

This paper seeks to let data define urban housing market segments, replacing the conventional administrative or any pre-defined boundaries used in the previous housing submarket literature. We model housing transaction data using a conventional hedonic function. The hedonic residuals are used to estimate an isotropic semi-variogram, from which residual variance–covariance matrix is constructed. The correlations between hedonic residuals are used as identifier to assign housing units into clusters. Standard submarket identification tests are applied to each cluster to examine the segmentation of housing market. The results are compared with the prevailing structure of market segments. Weighted mean square test shows that the defined submarket structure can improve the precision of price prediction by 17.5%. This paper is experimental in the sense that it represents one of the first attempts at investigating market segmentation through house price spatial autocorrelations.     

The Construction of Residential Housing Price Indices: A Comparison of Repeat-Sales,Hedonic-Regression,and Hybrid Approaches

This article examines a number of hypotheses that underpin the repeat-sales and hedonic approaches to the construction of housing price indices, as well as the practical problems associated with the implementation of either approach. We also examine a hybrid procedure that combines elements of both the repeat-sales and hedonic-regression techniques. For our sample of individual home sales in Oakland and Fremont California over an 18-year period, repeat-sales methods are subject to sample selection bias; the maintained assumption of time constancy of implicit prices of housing attributes is violated; the repeat-sales estimator is extremely sensitive to influential observations; and the usual method used to correct for heteroskedasticity in repeat-sale housing returns is inappropriate in our sample. Hedonic techniques are better suited to contend with index number problems per se, as they can accommodate changing attribute prices over time. They also appear to give rise to more reliable estimates of price indices, as unusual observations have less effect on estimated price indices. Drawbacks of the hedonic approach include the usual concern with omitted attributes, and their effect on the estimated price index.     

Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna

This paper is motivated by two common challenges in hedonic price modeling: nonlinear price functions, which require flexible modeling approaches, and the inherent spatial heterogeneity in real estate markets. We apply additive mixed regression models (AMM) to estimate hedonic price equations for rents in Vienna. Non-linear effects of continuous covariates as well as a smooth time trend are modeled non-parametrically through P-splines. Unobserved district-specific heterogeneity is modeled in two ways: First, by location specific intercepts with the postal code serving as a location variable. Second, in order to permit spatial variation in the nonlinear price gradients, we introduce multiplicative scaling factors for nonlinear covariates. This allows highly nonlinear implicit price functions to vary within a regularized framework, accounting for district-specific spatial heterogeneity, which leads to a considerable improvement of model quality and predictive power. Our findings provide insight into the spatially heterogeneous structure of price gradients in Vienna, showing substantial spatial variation. Accounting for spatial heterogeneity in a very general way, this approach permits higher accuracy in prediction and allows for location-specific nonlinear rent index construction.     

The relationship between rents and prices of owner-occupied housing in Taiwan

This article analyzes the changes of equilibrium rent and equilibrium price of owner-occupied housing in Taiwan, and also computes the rent multiplier and its trend in the past ten years in Taiwan to show how the housing consumption and housing investment change. A hedonic rent equation and a hedonic housing price equation are built first. Then, we apply the Housing Survey Report data from 1979 to 1989, and employ ordinary-least squares method to estimate the two equations. Using estimated coefficients of the two equations, we compute the market rents for owner-occupied housing and the market prices for rental housing. Finally, the rent multipliers are calculated from the market rents and market prices. The article finds that (1) changes of housing prices in Taipei lead to price changes in Kaoshung, and the latter leads Taiwan province; (2) changes of rent are much smaller than the changes of housing price; and (3) housing prices in Taiwan increased drastically. We also find: (1) at the peak of the housing market cycle, the rent multiplier is extremely high; (2) the rent multiplier drops in the year after the peak year because the rent catches up; (3) the rent multiplier in Taipei is greater than that of Kaoshung, and the multiplier in Kaoshung is greater than that of Taiwan province; and (4) overall, the rent multiplier in Taiwan is much greater than that of the United States.     

Spatial Dependence and House Price Index Construction

Accurate estimation of prevailing metropolitan housing prices is important for both business and research investigations of housing and mortgage markets. This is typically done by constructing quality-adjusted house price indices from hedonic price regressions for given metropolitan areas. A major limitation of currently available indices is their insensitivity to the geographic location of dwellings within the metropolitan area. Indices are constructed based on models that do not incorporate the underlying spatial structure in housing data sets. In this article, we argue that spatial structure, especially spatial dependence latent in housing data sets, will affect the precision and accuracy of resulting price estimates. We illustrate the importance of spatial dependence in both the specification and estimation of hedonic price models. Assessments are made on the importance of spatial dependence both on parameter estimates and on the accuracy of resulting indices.     

Spatial Variation in the Determinants of House Prices and Apartment Rents in China

This paper provides an examination of China??s residential real estate market at the county level using data from that country??s 2000 census. The market is a new one, having only been fully established in 1998. The analysis in the paper is in the form of an aggregate (county-level) hedonic model specified in two versions. Global parameters results are estimated using spatial error model specifications while more local effects are estimated by geographically weighted regression. Global results are typical in that structural characteristics such as floor space and contextual characteristics such as level of in-migration are important in residential prices. Local results, however, indicate significant spatial variation in the effect of both structural amenities and locational context on housing prices. In a simpler specification, rents are shown to respond positively to both median house prices levels and the supply of apartments available at market prices, but also with significant spatial variation across China.     

Hedonic price models and indices based on boosting applied to the Dutch housing market

We create a hedonic price model for house prices for six geographical submarkets in the Netherlands. Our model is based on a recent data-mining technique called boosting. Boosting is an ensemble technique that combines multiple models, in our case decision trees, into a combined prediction. Boosting enables capturing of complex nonlinear relationships and interaction effects between input variables. We report mean relative errors and mean absolute error for all regions and compare our models with a standard linear regression approach. Our model improves prediction performance by up to 39% compared with linear regression and by up to 20% compared with a log-linear regression model. Next, we interpret the boosted models: we determine the most influential characteristics and graphically depict the relationship between the most important input variables and the house price. We find the size of the house to be the most important input for all but one region, and find some interesting nonlinear relationships between inputs and price. Finally, we construct hedonic price indices and compare these with the mean and median index and find that these indices differ notably in the urban regions of Amsterdam and Rotterdam. Copyright © 2008 John Wiley & Sons, Ltd.     

Institutional Equity Trading Costs: NYSE Versus Nasdaq

We compare execution costs (market impact plus commission) on the New York Stock Exchange (NYSE) and Nasdaq for institutional investors. The differences in cost generally conform to each market's area of specialization. Controlling for firm size, trade size, and the money management firm's identity, costs are lower on Nasdaq for trades in comparatively smaller firms, while costs for trading the larger stocks are lower on NYSE. The cost differences estimated from a regression model are, however, sensitive to the choice of time period.     

Ownership Duration in the Residential Housing Market: The Influence of Structure,Tenure, Household and Neighborhood Factors

Turnover rates are important as determinants of the level of activity in housing related industries, in effecting housing market adjustments, and in revealing prices in illiquid, highly segmented, informationally inefficient housing markets. This study examines the relative influence of structure features, tenure, household characteristics and neighborhood factors on ownership turnover rates. The study exploits a Chicago database of just under 50,000 paired sales of attached housing units, with at least one of the sales occurring between 1992 and June of 2002. Within the framework of a Cox proportional hazard model, we focus on a number of factors affecting turnover rates, including whether the housing unit is owner-occupied or rented at the time of sale, price at the time of sale, unit size, age, location in a tax increment financing district, housing density, structure size, year of sale, and neighborhood within Chicago (by Community Area). Finding strong spatial segmentation in turnover (hazard) rates, we further examine the capacity of four sets of Census-derived variables to explain the spatial variation. The household characteristics offer decidedly the strongest power in explaining the segmentation. Results from the hazard model, combined with results from the analysis of spatial variation suggest a household life cycle model of variation in turnover rates.     

The Value of Smoking Prohibitions in Vacation Rental Properties

We examine the value of smoking prohibitions by developing a model of the rent differential between smoking and nonsmoking properties. We empirically test for the rent differential using a data set of vacation rental properties from the Outer Banks of North Carolina. Given peak season rents, hedonic variables such as oceanfront location, and number of bedrooms and bathrooms price according to expectations. Distance from vehicular congestion also leads to greater rent, reflecting vacationer desires for beauty as well as peace and quiet. Most significantly, our results reveal that vacationers are willing to pay substantial additional rent for properties that prohibit smoking. Understanding the demand for smoking prohibitions is important to academics, professionals, and others associated with owning, operating, and financing real estate.     

A Primer on Piecewise Parabolic Multiple Regression Analysis via Estimations of Chicago CBD Land Prices

This article proposes a non-parametric method for estimating spatial price functions. Space is divided into squares. The independent variables are barycentric coordinates that uniquely describe the location of observations in space. The regression coefficients are estimates of the height of the function directly over the vertices of the square spatial units. Within each square the function has hyperbolic iso-price curves and parabolic sections. The price function is continuous, but not differentiable, at the boundaries between contiguous squares. This method is applied to the problem of describing the price per front foot of land in the Chicago CBD. A rather complex price surface is revealed that would be difficult to estimate using other methodologies but was easily estimated by this simple method.     

EMPIRICAL ANALYSIS ON THE PREDICTORS OF FUTURE SPOT RATES

This paper examines the issue of the prediction of future spot rates by applying the seemingly unrelated regression technique to four major currencies using data from January 1974 to September 1982. The empirical evidence indicates that current spot rates provide a better prediction of future spot rates than do current forward rates. In further rolling subsample studies, the estimated coefficients for current forward rates (or spot rates) are found to be sensitive to the new information. An important implication of this paper is that since the estimated coefficients vary over time, the underlying pattern of the generated coefficients should be extrapolated and incorporated into the exchange rate predictions.     

Valuing Open Space and Land-Use Patterns in Urban Watersheds

This article presents the results of a hedonic property value analysis for an urban watershed in New Haven County, Connecticut. We use spatially referenced housing and land-use data to capture the effect of environmental variables around the house location. We calculate and incorporate data on open space, land-use diversity, and other environmental variables to capture spatial variation in environmental quality around each house location. We are ultimately interested in determining whether variables that are reflective of spatial diversity do a better job of describing human preferences for housing choice than broad categories of rural versus urban areas. Using a rich data set of over 4,000 houses, we study these effects within a watershed that includes areas of high environmental quality and low environmental quality as well as varying patterns of socioeconomic conditions. Our results suggest that, in addition to structural characteristics, variables describing neighborhood socioeconomic characteristics and variables describing land use and environmental quality are influential in determining human values. We also find that the scale at which we measure these spatially defined environmental variables is important.     

A hedonic price model for private properties in Hong Kong

A hedonic model is used to explore the effects of locational, structural, and neighborhood attributes on the price structure of private condominiums in Hong Kong. The regression results and the elasticities of housing attributes obtained from the Box-Cox analysis indicate that the valuation of a property is sensitive to changes in housing traits. Home buyers are rational and are willing (unwilling) to pay for desirable (undesirable) housing attributes and that the valuation of a property is market-driven in Hong Kong.     

Anisotropic Autocorrelation in House Prices

This article examines anisotropic spatial autocorrelation in single-family house prices and in hedonic house-price equation residuals using a spherical semivariogram and transactions data for one county in the Philadelphia, Pennsylvania, MSA. Isotropic semivariograms model spatial relationships as a function of the distance separating properties in space. Anisotropic semivariograms model spatial relationships as a function of both the distance and the direction separating observations in space. The goals of this article are (1) to determine whether there is spatial autocorrelation in hedonic house-price equation residuals and (2) to empirically examine the validity of the isotropy assumption. We estimate the parameters of spherical semivariograms for house prices and for hedonic house-price equation residuals for 21 housing submarkets within Montgomery County, Pennsylvania. These housing submarkets are constructed by dividing the county into 21 groupings of economically similar adjacent census tracts. Census tracts are grouped according to 1990 census tract median house prices and according to characteristics of the housing stock. We fit the residuals of each submarket hedonic house price equation to both isotropic and anisotropic spherical semivariograms. We find evidence of spatial autocorrelation in the hedonic residuals in spite of a very elaborate hedonic specification. Additionally, we have determined that, in some submarkets, the spatial autocorrelation in the hedonic residuals is anisotropic rather than isotropic. The empirical results suggest that the spatial autocorrelation in Montgomery County single-family house-price equation residuals is anisotropic in submarkets where residents typically commute to a regional or local central business district.