A Varying Parameters Approach to Constructing House Price Indexes

Conventional housing price index models assume interperiodparameter stability and typically employ either repeat sales or hedonic methodologies. This paper introduces a method of index construction that combines multiple sales observations with single sale transactions while permitting characteristics prices from hedonic regressions to vary over time. A test for interperiod parameter stability is provided. Each period's data are arranged by location and repeat sales are matched by rows. This construction allows greater use of sample information and acknowledges the unique contribution of repeat sales to the estimation process. It also produces intertemporal error correlations that can be beneficially exploited by the seemingly unrelated regressions (SUH) technique. The paper also demonstrates a significance test for error correlation and discusses the treatment of unequal numbers of observations among index periods.     

Spatial Competition and Shopping Externalities: Evidence from the Housing Market

In search markets, greater spatial concentration of sellers increases price competition. At the same time, though, a greater concentration of sellers can create a shopping externality by attracting more buyers to the site. Using housing sales data, we test for spatial competition and shopping externality effects on prices and marketing time. We find that they reflect both competitive and shopping externality effects from surrounding houses, although the relative strength varies with how fresh the house is in the market, the freshness of surrounding houses, and the phase of the market cycle. New listings have the strongest shopping externality effect on neighboring houses that have been on the market for some time. Vacant houses have their strongest competition effects in the declining market and externality effects in the rising market. Fresh houses on the market reap little benefit from shopping externalities in all phases of the market cycle.     

Individual Agents,Firms, and the Real Estate Brokerage Process

This study examines how individual agents affect house selling prices and time on the market while controlling for brokerage firm-specific effects as well as supply and demand conditions that vary by neighborhood. Firm size effects disappear once firm specialization and agent characteristics are taken into account but geographic concentration by firms leads to higher selling prices. For individual agents, neither sex nor selling own listings affects price or selling time, but there are gains from partnering transactions across firms. Agents who specialize in listing properties obtain higher prices for their sellers while those who specialize in selling obtain lower prices for their buyers. Houses nearer to other transactions of an agent sell for higher prices. Finally, greater scale of listing and selling activity by an agent tends to lower selling price or lengthen the time on the market.

A Complete Nonparametric Event Study Approach

Event studies have been used to examine the direction, magnitude, and speed of security price reactions to various phenomenon. Concerns over the lack of normality in stock return distributions motivated the introduction of nonparametric test statistics in the event study literature. A parametric procedure (OLS), however, has been extensively employed in the estimation of parameters for the market model. This paper, in contrast, applies Theil's nonparametric regression in the estimation of abnormal returns; an approach which is distribution free and provides a complete nonparametric approach for the detection of abnormal performance. Simulation results indicate Theil's estimation procedure offers a slight improvement in power in the detection of abnormal performance over the traditionally employed methodology. The results suggest employing Theil's nonparametric estimation procedure combined with the rank statistic. This complete nonparametric combination offers similar power with fewer underlying assumptions.     

A Flexible Fourier Approach to Repeat Sales Price Indexes

Time periods are typically highly aggregated for repeat sales estimators because of the small number of observations available in some periods. We use a flexible Fourier expansion to account for time, which we treat as a continuous variable. Our estimator saves degrees of freedom and enables us to estimate the price index efficiently even for times with few sales. We present estimated price indexes for the City of Chicago, Cook County, and several suburbs.     

Trends in Real Estate Research, 1988–2001: What's Hot and What's Not

This paper examines publications and citations for topic and technique trends in JREFE and REE during the period 1988–2001. Publication and citation patterns reveal real estate as a largely empirical field. The mix of topics and techniques published by the two journals as well as sources cited and garnering citations are converging over time. Within topic or technique categories, the most frequently cited papers or authors tend to garner citations that vary with the relative popularity of the category in the broader literature. Yet, the impacts of top contributors reflect significant contributions outside their most cited research in one specialized one topic area or technique.     

Aggregation Bias in Repeat-Sales Indices

The repeat-sales methodology has become a standard approach for estimating real estate price indices. This article examines the underlying assumptions inherent in the repeat sales model and provides an empirical test for both included and omitted variables as sources of aggregation bias. The results indicate that virtually all price indices may be biased, the degree of bias being dependent upon the number of variables examined and the instability of their parameters over time.     

Individual and Institutional Contributors to the Journal of Real Estate Finance and Economics: 1988–1999

This article identifies the individuals and institutions contributing the research published in the Journal of Real Estate Finance and Economics during its first 12 years. Consistent with the trend in many fields, coauthorship has increased during the period. The top individual contributors vary across six-year periods as well as whether measured by appearances or pages. The concentration of publications by the top individuals has decreased over time. Unlike individuals, the top institutions are not sensitive to whether measured by appearances or pages or to whether measured using contemporaneous or current faculty affiliations. The breadth of the journal appears to be growing; contributions by universities outside the United States and by private firms and organizations have risen during the 12 years.     

Big House, Little House: Relative Size and Value

How do markets value relative house size in a neighborhood? The literature offers differing rationales: atypical houses sell for less, capitalization of property taxes penalizes larger and benefits smaller houses in mixed neighborhoods and conspicuous consumption reinforces the value of relatively larger houses and reduces the value of relatively smaller houses to consumers. Using a simultaneous price‐liquidity model that controls for neighborhood supply and demand conditions, this article finds a dominant tax capitalization effect on price and marketing time that appears to override any extant atypicality or conspicuous consumption effects.