A Semiparametric Method for Estimating Local House Price Indices

Spatial autoregressive hedonic models utilize house prices lagged in space and time to produce local house price indices, for example, the spatial and temporal autoregressive (STAR) model might be used this way. This paper complements these models with a semiparametric approach, the Local Regression Model (LRM). The greater flexibility of the LRM may allow it to identify space–time asymmetries missed by other models. The LRM is fitted to 49,511 sales from 1972Q1 to 1991Q2 in Fairfax County, Virginia. The local price indices display plausible and significant variations over space and time. The LRM price indices in selected neighborhoods are shown to differ significantly from those in some other neighborhoods. A new method for estimating standard errors addresses an overlooked problem common to all local price indices: how to evaluate the amount of noise in the estimates. Out-of-sample prediction errors demonstrate that LRM adds significant information to the hedonic model.     

GMM Repeat Sales Price Indices

Illiquid assets are widely spread within the economy but their indices are difficult to measure. This paper proposes a Generalized Method of Moment (GMM) repeat sales regression for estimating illiquid asset price indices. This method has estimators that are arithmetic averages of individual asset returns. This method is able to estimate custom-weighted indices, including equal- and value-weighted indices. It can incorporate hedonic variables to improve estimation accuracy, and it can work with a reweighting technique to mitigate a biased sample problem. Simulations based on artificial markets indicate that the method is more accurate than some alternatives in both efficient and sluggish markets, with and without temporal aggregation. As an application, we use this method to estimate a commercial property price index.     

Predicting Spatial Patterns of House Prices Using LPR and Bayesian Smoothing

This article is motivated by the limited ability of standard hedonic price equations to deal with spatial variation in house prices. Spatial patterns of house prices can be viewed as the sum of many causal factors: Access to the central business district is associated with a house price gradient; access to decentralized employment subcenters causes more localized changes in house prices; in addition, neighborhood amenities (and disamenities) can cause house prices to change rapidly over relatively short distances. Spatial prediction (e.g., for an automated valuation system) requires models that can deal with all of these sources of spatial variation. We propose to accommodate these factors using a standard hedonic framework but incoporating a semiparametric model with structure in the residuals modeled with a partially Bayesian approach. The Bayesian framework enables us to provide complete inference in the form of a posterior distribution for each model parameter. Our model allows prediction at sampled or unsampled locations as well as prediction interval estimates. The nonparametric part of our model allows sufficient flexibility to find substantial spatial variation in house values. The parameters of the kriging model provide further insights into spatial patterns. Out–of–sample mean squared error and related statistics validate the proposed methods and justify their use for spatial prediction of house values.     

The Economics of Style: Measuring the Price Effect of Neo‐Traditional Architecture in Housing

Vintage effects have received considerable attention from economists in the context of house prices. Although strongly related, the impact of architectural building styles on prices has not been studied yet. Using a cross‐sectional hedonic price analysis including building styles of recently developed homes in the Netherlands we find a significant price premium for housing with neo‐traditional architecture. Extensive intervention by local authorities on the supply side of the housing market seems the most probable explanation of this effect. The decreasing price premium over time reflects the impact of supply restrictions on price, but also indicates that style does matter.     

Temporal Aggregation in Real Estate Return Indices

Temporal aggregation is defined as the use of spot valuations of properties occuring over an interval of time to impute the spot value of a property or of a real estate value index as of a single point in time. Temporal aggregation may characterize not only appraisal-based indices but also indices based directly on transaction prices, such as the National Real Estate Index (NREI) and regression-based indices such as hedonic or repeat-sales indices. This paper analyzes the effect of temporal aggregation on the smoothing of the time series second moments in the resulting real estate return index. Assuming true spot returns are uncorrelated, temporal aggregation-induced smoothing will cause the empirically observed real estate index to understate the own-variance by one-third and the beta by one-half. This amount of bias in the second moments can have major implications for the real estate share in an optimal portfolio. Thus, empirical-based investment analysis could be led astray by smoothing even if the real estate return index is "transaction-based" rather than "appraisal-based."     

The Spatial Proximity of Metropolitan Area Housing Submarkets

An important question related to housing submarket construction is whether geographic areas must be spatially adjacent in order to be considered the same submarket. Housing consumers do not necessarily limit their search to spatially concentrated areas and may search similarly priced neighborhoods located throughout a metropolitan area when making housing consumption decisions. This article examines two alternative procedures for delineating submarkets: one that combines adjacent census block groups into areas with enough transactions to estimate the parameters of a hedonic house price equation and a second that permits spatial discontinuities in submarkets. The criterion used to evaluate the alternative techniques is the accuracy of hedonic house price predictions.     

Variation Across Price Segments and Locations: A Comprehensive Quantile Regression Analysis of the Sydney Housing Market

Standard house price indices measure average movements of average houses in average locations belonging to an average price segment and hence obscure spatial and cross‐sectional variation of price appreciation rates even within a single metropolitan area. This article combines penalized quantile regression techniques with the hedonic imputation approach to reveal such kind of variation. The method is applied to house transactions from Sydney between 2001 and 2014. The analysis finds significant variation across sub‐markets over time and in particular during the boom‐and‐bust cycle peaking in 2004. Appreciation rates were highest for suburban, low‐priced and lowest for inner‐city, high‐priced houses.     

Do House Price Levels Anticipate Subsequent Price Changes within Metropolitan Areas?

This research examines the relationship between hedonically controlled housing price levels and subsequent changes in those prices across locations within MSAs. Are areas with a high price relative to an “imputed rent” paying for higher appreciation? In an efficient market (e.g., Gordon Growth Model), as fundamentals (impute rent) differ across locations and change over time, anticipation of these should generate a positive correlation between (residual) price levels and subsequent price changes. We undertake these tests in four different MSAs using a panel of repeat‐sale house price indices that have been scaled to price levels with the hedonic attributes of the house and ZIP code. In three markets we find that identical houses in higher priced ZIP codes subsequently appreciate faster. In one market we find that there is little statistical difference.     

Alternative Housing Price Indices: An Evaluation

This paper reports on research in which eleven alternative housing price indices are constructed for two Vancouver neighbourhoods for the period from 1957 to 1979. Three criteria for good indices are presented, and the eleven indices, as well as several government and industry indices are evaluated in accordance with those criteria. It is determined that, almost surprisingly, an index based on mean sales values performs well, as do several of the hedonic price equation based indices. Several policy implications of the analysis are then discussed.     

Transaction-Based Office Price Indexes: A Spatiotemporal Modeling Approach

This study examines the potential of a two-order spatiotemporal autoregressive model with a Bayesian heteroskedasticity robust procedure in modeling strata-titled Singapore office unit transaction prices and in constructing transaction-based disaggregate office price indexes. The model reduces the problems caused by the infrequent trading of individual commercial properties. However, for those office properties that are located outside the CBD and also for those less frequently transacted, the power of the model in capturing these particular office buildings' price dynamics is limited. The significant differences of the office prices across the various office buildings and submarkets show that the model can capture the variation in office prices and track the timing of capital gains and losses that investors may accrue on spatially distributed office properties more accurately than hedonic or weighted least squares estimates.     

Loss Aversion and Anchoring in Commercial Real Estate Pricing: Empirical Evidence and Price Index Implications

Loss aversion behavior plays a major role in the pricing of commercial properties, and it varies both across the type of market participants and across the cycle. We find that sophisticated and more experienced investors are at least as loss averse as their counterparts and that loss aversion operated most strongly during the cycle peak in 2007. We also document a possible anchoring effect of the asking price in influencing buyer valuation and subsequent transaction price. We demonstrate the importance of behavioral phenomena in constructing hedonic price indices, and we find that the impact of loss aversion is attenuated at the aggregate market level. This suggests that the pricing and volume cycle during 2001–2009 was little affected by loss aversion.     

To Sell or Not to Sell: Measuring the Heat of the Housing Market

Combining list‐price, sale‐price and time‐on‐the‐market data, we estimate an index that summarizes housing market conditions and that has a direct economic interpretation. The index measures seller's bargaining power in a structural search model of home seller behavior. Structural estimation uncovers an analytical relationship between reduced form coefficients of hedonic and marketing‐time equations and structural parameters. Thus, the index can be estimated using individual‐level or aggregate data. Using housing transactions data from the Washington, D.C., area, we show that index trends coincide with the up and downturns in home appreciation rates and with popular perceptions about the “heat” of the market.     

Chicago's Office Market: Price Indices, Location and Time

Conventional wisdom holds that overbuilding and high vacancy, coupled with curtailed tax benefits, have led to reduced office property values since the late 1980s. Yet assertions that office real estate values fell between the mid-1980s and mid-1990s are not supported everywhere by convincing evidence. This study offers a hedonic analysis of Chicago area office properties that sold from 1986 through 1993. Whereas earlier office market studies generally have been based on rents, this study focuses on variation in actual sale prices (although the prices were not adjusted for financing differences). The transaction-based index estimated here does not support the existence of a nominal office property price level decline beginning in the mid-to-late 1980s. In fact, the results show an upward trend in office property values after 1986, with nominal declines in office market price levels occurring only in the latter portion of the study period.     

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.     

A Long Run House Price Index: The Herengracht Index, 1628–1973

This article introduces a biennial historic index of real estate values for the period 1628 through 1973. This index is based on the transactions of the buildings on the Herengracht, one of the canals in Amsterdam. Since its development, the quality of the buildings on this canal has been on a constant, high level, which makes the Herengracht a unique sample to base a long run house price index upon. The index is a hedonic repeated-measures index and is estimated in real terms. An index is also constructed in nominal terms. The average real price increase after World War II is about 3.2% per annum. Nevertheless, the real value of the index in 1973 is only twice as high as it was in 1628.     

Graduated Flood Risks and Property Prices in Galveston County

Our hedonic property analysis approach in Galveston County, Texas aims at estimating the impacts of flood risks and water‐related amenities in a more systematic way. First, we interact distance to the nearest coastline and flood risk in order to account for these impacts acting together on housing sales prices in our coastal community. Second, we use more granular flood risk measure in the analysis compared to the existing literature. Results show that the hedonic price effect is dependent upon the distance to the nearest coastline, and as expected the distance effect varies by flood risk type. We find that in this coastal housing market properties located in the highest risk flood area, for up to nearly a quarter mile from the nearest coastline, actually command a price premium. A recent movement toward risk‐based flood insurance premiums in the United States was deeply opposed by the real estate sector for fear of causing property values to steeply decline. This analysis sheds some further light on this depressed property value assertion highlighting its sensitivity to distance to the water.     

From Central Planning to Centrality: Krakow's Land Prices After Poland's Big Bang

We examine commercial land markets in Krakow, Poland over a 10-year period of transition from socialist management to a market economy. We explore the spatial and temporal evolution of land prices over this period. In particular, we are interested in identifying trends toward or away from centrality, and in discovering whether or not these trends acted on the city center alone or over a set of centers. The data set we employ is uniquely appropriate for this purpose as the densifying force of "highest-and-best" use—typically found in market-oriented cities—was absent under four decades of socialist planning, leaving undeveloped land scattered throughout the city. Free of quality control issues associated with disentangling the value of land from properties in which land and structures are bundled, the data offer a clean assessment of land prices within an urban area. We employ a novel, iterative approach to identify pricing centers—"nodes" of similarly sized residuals—which we interpret as evidence of omitted spatial amenities. Using this approach, we find that the price gradient in Krakow evolved toward concentration, but concentration in several centers rather than in just one. We find that the exclusion of proximity to these centers leads to biased coefficients in the hedonic regressions; we also find that the majority of the apparent spatial autocorrelation in the aspatial regressions results from the omission of proximity to these centers.     

Deed Types, House Prices and Mortgage Interest Rates

When houses are sold they come with a deed attached that spells out the legal guarantees on good title. Some deeds give clues about the characteristics of the seller or the house. Using a 37,043-observation house price hedonic with a Bayesian spatial error model, we find the type of deed attached to a housing sale can have a dramatic correlation with the sale price. Ten deed types command a discount, and one commands a premium relative to warranty deeds. Mortgage rates for sheriff's deeds and foreclosure deeds are lower than for warranty deeds, indicating more sophisticated buyers.     

Localized Competition,Heterogeneous Firms and Vertical Relations

This paper investigates the link between firms' geographic configuration and market power in imperfect markets. We consider two related setups. The first illustrates the relevant characteristics of the pricing equilibrium. A main implication is that the equilibrium price vector changes in accordance with the firms' spatial configuration. The second, where firms operate as downstream retailers affiliated to rival upstream wholesalers, shows that upstream market power is strongly affected by an index of geographic concentration which reflects the spatial configuration of retailers. Finally, our analysis provides several insights for market delineation as well as merger evaluation and remedies.     

Interpretable machine learning for real estate market analysis

Machine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out-of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model-agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U-shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methods are also able to visualise how the strength and interactions of hedonic characteristics change over time, which investors could use to determine the types of assets that perform best at any given stage of the real estate investment cycle.