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Valuation Modeling within Thin Housing Markets Case Study: Arab Housing Market in Israel
Abstract
The primary aim of this paper is to introduce valuation modeling applicable to thin housing markets, with a focus on the Arab housing sector in Israel. The estimation procedure utilizes two input values: transaction data and subjective valuations provided by property owners, the data for which are derived from the Israel Tax Authority (ITA) and the Household Expenditure Survey (HES). Average property values are also weighted and ranked according to location, size, and average income factors. The main contribution of these modeling techniques is that they can be employed to estimate the residential property values in markets that experience a low frequency of housing transactions and where information is limited, with the added benefit of understanding housing value movement and market dynamics. Housing policies could be influenced by this deeper understanding of house price behavior within localities and submarkets, potentially with the ability to monitor changes in dwelling values and segmentation and segregation effects.
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This chapter explores the opportunities raised by the application of fuzzy
logic, fuzzy sets and number to property valuation and mass appraisal. Fuzzy
logic and fuzzy number have been applied to the property market in sev
eral works (Sui, 1992; Byrne, 1995; Del Giudice Amabile, 1997; Smith and
Bagnoli, 1997, 1998) developing an alternative and flexible approach to uncer
tainty. The contributions that have used fuzzy logic may be divided in two
groups. The former is composed of scientific works that use fuzzy logic and
fuzzy sets as a way to define property value in a real estate market base with
uncertain information. The latter group uses fuzzy logic, fuzzy sets and fuzzy
system controls to improve property valuation methodologies to make prop
erty valuation methods closer to human behaviour. The first and the second
section will focus attention on property value. In the second section, the def
inition of 'most possible selling price' will be recalled. In the third section
attention will be drawn to the ranges of value both in probabilistic (Medici,
1953) and possibilistic frameworks using fuzzy numbers. Both the second and the third sections will be devoted to the group of scientific works that
use fuzzy logic and fuzzy numbers as a way to define value in uncertain
contexts. The fourth section is focused on the relationship between fuzzy
logic and property valuation methodology. This section will be devoted to
the second group of scientific works which analyse the contribution of fuzzy
logic to enhance property valuation methodologies. Finally, the fifth section
highlights the contribution of fuzzy logic to mass appraisal method in emerg
ing markets and, in particular, the Belorusian context. Final remarks will be
offered at the end of the chapter.
Mass appraisal valuation is increasingly based on multiple regression analysis in order to fit valuation statistical model for calculating market value of all properties in a target population. In this paper we develop and assess the predictive performance of different models for the estimation of dwelling values for all types of dwellings at a nationwide level in Israel. In order to investigate the representativeness of transaction data, the regression coefficients derived from the hedonic models estimated on transaction prices and on self-reported dwelling valuations, are compared. The results of this analysis are used to build several prediction models. Regression coefficients' stability is tested by a quantile regression method that allows properties to be divided into sufficiently homogeneous estimation cells parsed by geographic and economic criteria. Our prediction model allowed obtaining acceptably precise dwelling value estimators at a census tract level. The proposed method applied to additional time points, gains stable estimators with rather small fluctuations in accuracy indices and standard deviations. Dwelling values estimated at the nationwide level enable to produce new statistical products at high geographic resolutions on a range of topics, e.g., the behavior of the housing market, the economic profile of residential areas, well-being and inequality, etc.
In order to characterize the socioeconomic profile of various geographic units, it is common practice to use aggregated indices. However, the process of calculating such indices requires a wide variety of variables from various data sources available concurrently. Using a number of administrative databases for 2001 and 2003, this study examines the question of whether dwelling prices in a given locality can serve as a proxy for its socioeconomic level. Based on statistical and geographic criteria, we developed a Dwelling Price Ranking (DPR) methodology. Our findings show that the DPR can serve as a good approximation for the socioeconomic cluster (SEC) calculated by the Israel Central Bureau of Statistics for years when the required data was available. As opposed to the SEC, the suggested DPR indicator can easily be calculated, thus ensuring a continuum of socioeconomic index series. Both parametric and nonparametric statistical analyses have been carried out in order to examine the additional social, demographic, location, crime and security effects that are exogenous to SEC. Complementary analysis on recently published SEC series for 2006 and 2008 show that our conclusions remain valid. The proposed methodology and the obtained findings may be applicable for different statistical purposes in other countries which possess dwelling transactions data.
When property tax assessment ratios vary, the costs of public services are unevenly redistributed. More sales in a census tract should help to improve assessment uniformity while providing homeowners with a stronger basis for appeals. Using data from Chicago to estimate a multinomial logit model that characterizes the distribution of assessment ratios, we find that a variable measuring sales frequency is highly significant with the predicted effect: both unusually high and low ratios are more likely to occur in areas with few comparable sales. We find less evidence to support the notion that thin markets are responsible for regressive distributions, whereby assessment ratios are higher for low-value homes than they are for high-value ones. Accountingfor sales frequency reduces but does not eliminate our finding ofregressivity.
Owners' valuations of dwelling prices are central in construction of price indices, empirical research of housing markets and households' economic behavior. Previous studies show that, on average, owners tend to overestimate the value of their dwellings by 5% relative to market valuation. We analyze the variation of the bias over the distribution of dwelling sale prices, using a unique dataset of more than 22,000 observations from Israel's Household Expenditure Survey, from 1997 to 2008, merged with the national sample of housing sale transactions by census tract. We find that self-reported estimates of dwelling values are, on average, 27% higher than the mean market prices of houses in the corresponding census tracts. Strikingly, the valuations of inexpensive and costly dwellings are biased in different directions: estimates reported by people who occupy dwellings in the lowest eight deciles of the price distribution are upward-biased, whereas those who live in the most expensive dwellings more typically understate the value of their homes. The self-reported valuation bias is systematically associated with owner's traits as well as with dwelling and neighborhood characteristics. The frequency of dwelling sales in the respondent's tract was found to have an effect on the self-reported valuation bias.
Hedonic house price models are increasingly applied in the process of mass appraisal, in which econometric specifications are used to obtain automated valuation of properties for taxation purposes. The predictive quality of such models is important, since it directly affects the revenue stream of local authorities. In this paper, we assess the relative predictive performance of different model specifications used in automated valuation. Specifically, we focus on the issue of spatial heterogeneity by comparing models that utilize different definitions of housing submarkets. In addition, we consider the inclusion of ‘spatial’ explanatory variables in the form of distance to various amenities as computed from a GIS. We apply this to data from the city of Bogotá, Colombia, a pioneer in the application of mass appraisal techniques in a developing country context. We find that specifications that include the submarkets improve predictive performance and that the inclusion of the spatial variables is superior to the traditional models of homogenous zones. However, even the best models are still characterized by relatively poor performance in the form of a high degree of overprediction of the house value. In addition, the predictive performance of the models varied by socio‐economic stratum in the city, which suggests that the dynamics of the housing markets in these strata would require closer and separate attention. These results may provide further guidance to enhance mass appraisal practice in the city of Bogotá as well as potentially other Latin American cities.
Resumen. Los modelos hedónicos de precios de la vivienda se están aplicando cada vez más en procesos de tasación en masa, en los que las especificaciones econométricas se utilizan para obtener una valoración automatizada de propiedades con fines fiscales. La calidad predictiva de dichos modelos es importante ya que afecta directamente a los ingresos de las autoridades locales. En este artículo evaluamos el comportamiento predictivo relativo de diferentes especificaciones del modelo utilizadas en la tasación automatizada. Específicamente, nos fijamos en el problema de la heterogeneidad espacial mediante la comparación de modelos que utilizan definiciones diferentes para los submercados de la vivienda. Consideramos además la incorporación de variables explicativas ‘espaciales’ representadas por la distancia a diferentes servicios y calculadas mediante SIG . Aplicamos todo ello a datos de la ciudad de B ogotá, C olombia, pionera en la aplicación de técnicas de tasación en masa bajo un contexto de países en desarrollo. Descubrimos que las especificaciones que incluyen los submercados mejoran el comportamiento predictivo y que la incorporación de variables espaciales es superior a los modelos tradicionales de zonas homogéneas. Sin embargo, incluso los mejores modelos aun están caracterizados por un pobre comportamiento relativo en cuanto a un alto grado de sobrepredicción del valor de la vivienda. Asimismo, hubo variación en el comportamiento predictivo de los modelos de acuerdo con el estrato socio‐económico de la ciudad, lo que sugiere que las dinámicas de los mercados de la vivienda en estos estratos podrían requerir una atención más detallada por separado. Estos resultados podrían proporcionar pautas adicionales para mejorar las prácticas de tasación en masa en la ciudad de B ogotá, así como posiblemente en otras ciudades latinoamericanas.
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Home value plays an important role in a variety of fields of research. In this paper, three commonly used measures of home value are evaluated and compared using a framework developed by Jöreskog and Goldberger (1975) for estimation of causal models containing unobserved variables. The paper extends ideas presented by Kain and Quigley (1972) and Kish and Lansing (1954).
Various location specific attributes cause segmentation of the housing market into submarkets. The question is, whether the most relevant partitioning criteria are directly related to the transaction price or to other, socio-economic and physical, features of the location. On the empirical side, several methods have been proposed that might be able to capture this influence. This paper examines one of these methods: neural network modelling with an application to the housing market of Helsinki, Finland. The exercise shows how it is possible to identify various dimensions of housing submarket formation by uncovering patterns in the dataset, and also shows the classification abilities of two neural network techniques: the self-organising map (SOM) and the learning vector quantisation (LVQ). In Helsinki, submarket formation clearly depends on two factors: relative location and house type. Price-level clearly has a smaller role in this respect.
The aim of this paper is to attempt to measure the effect of location on residential house prices and to endeavour to integrate spatial and aspatial data in terms of developing a hybrid predictive model. The research methodology investigates the traditional hedonic approach to modelling location using multiple regression techniques. Alternative approaches are considered which specifically model the spatial distribution of house prices with the objective of developing location adjustment factors. These approaches are based on the development of surface response techniques such as inverse distance weighting and universal kriging. The results generated from the surfaces created are then calibrated within MRA.
Purpose
– Tobler's law of geography states that things that are close to one another tend to be more alike than things that are far apart. In this regard, the spatial pattern of price distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The purpose of this paper is to provide emerging findings of research analysing the salient factors which impact on the sale price of residential properties using a spatial regression approach.
Design/methodology/approach
– The research develops and formulates a geographically weighted regression (GWR) model to incorporate residential sales transactions within the Belfast Metropolitan Area over the course of 2010. Transaction data were sourced from the University of Ulster House Price Index survey (2010, Q1‐Q4). The GWR approach was then evaluated relative to a standard hedonic model to determine the spatial heterogeneity of residential property price within the Belfast Metropolitan Area.
Findings
– This investigation finds that the GWR technique provides increased accuracy in predicting marginal price estimates, in comparison with traditional hedonic modelling, within the Belfast housing market.
Originality/value
– This study is one of only a few investigations of spatial house price variation applying the GWR methodology within the confines of a UK housing market. In this respect it enhances applied based knowledge and understanding of geographically weighted regression.
Focus This case study presents an introduction to the basics of real estate appraisal and multiple regression analysis; in particular, as used in real estate valuation for mass property tax assessment. While real estate researchers, appraisers and some tax assessors have used multiple regression analysis for many years, its use by a large number of assessors is relatively new. The purpose of this case is to expose students to standard appraisal approaches including the market comparison technique as well as the advantages and disadvantages of using multiple regression analysis. In their answers to the case, students are encouraged to explore and develop solutions, so as to understand how to use the market comparison approach and multiple regression analysis for real estate valuation. Setting The real estate tax assessment process is used to provide an introduction to multiple regression analysis. The tax assessor's office in a small west Texas county has always assessed properties through manual market comparison analysis. This manual process uses recently sold properties that are in close proximity to the subject property to make corresponding weighted adjustments. After going to a seminar on multiple regression analysis for mass appraisals, the county tax assessor employs a university professor to explain how multiple regression analysis works for real estate valuation and mass assessment, as well as what its relative benefits are over the existing manual system. He invites his staff, the county commissioners, and others to a one-night seminar that explains multiple regression analysis. This seminar presented by a university professor to Texas participants is used educate case readers about real estate appraisal and multiple regression analysis.
Using data on house sales and inventories, this paper shows that housing transactions are driven mainly by listings and less so by transaction speed, thus the decision to move house is key to understanding the housing market. The paper builds a model where moving house is essentially an investment in match quality, implying that moving depends on macroeconomic developments and housing-market conditions. The number of transactions has implications for welfare because each transaction reduces mismatch for homeowners. The quantitative importance of the decision to move house is shown in understanding the U.S. housing-market boom during 1995–2003. (JEL: D83, E22, R31)
We maintain that the appropriate definition of submarkets depends on the use to which they will be put. For mass appraisal purposes, submarkets should be defined so that the accuracy of hedonic predictions will be optimized. Thus we test whether out-of-sample hedonic value predictions can be improved when a large urban housing market is divided into submarkets and we explore the effects of alternative definitions of submarkets on the accuracy of predictions. We compare a set of submarkets based on small geographical areas defined by real estate appraisers with a set of statistically generated submarkets consisting of dwellings that are similar but not necessarily contiguous. The empirical analysis uses a transactions database from Auckland, New Zealand. Price predictions are found to be most accurate when based on the housing market segmentation used by appraisers. We conclude that housing submarkets matter, and location plays the major role in explaining why they matter.
Based on annual household surveys between 2011 and 2014 and additional evidence from a customized survey in 2013, we document that a sizable group of homeowners in the Netherlands has a rosy picture of the value of their house. Even homeowners who are arguably well informed tend to overestimate the value of their house. Multivariate regression analyses suggest that this overestimation is positively related to the mortgage loan-to-value ratio and the tenure of the owner-occupier. Moreover, homeowners tend to overestimate the value of the house when the actual value falls below the purchase price of the house. We discuss rational interpretations of these findings as well as psychological explanations such as loss aversion or an endowment effect.
I examine the effects of seller uncertainty over their home value on the housing market. Using evidence from home listings and transactions data, I first show that sellers do not have full information about current period demand conditions for their homes. I incorporate this type of uncertainty into a dynamic microsearch model of the home selling problem with Bayesian learning. The estimated model highlights how information frictions help to explain the microdecisions of sellers and how these microdecisions affect aggregate market dynamics. The model generates a significant microfounded momentum effect in short-run aggregate price appreciation rates.
A house is made up of many characteristics, all of which may affect its value. Hedonic regression analysis is typically used to estimate the marginal contribution of these individual characteristics. This study provides a review of recent studies that have used hedonic modeling to estimate house prices. The findings indicate that slanted versus flat roof, sprinkler system, garden bath, separate shower stall, double oven and gated community positively affect selling price while not having attic space, living in an earthquake zone, proximity to a hog farm, proximity to a landfill, proximity to high voltage lines, corporate-owned properties, percentage of Blacks or Hispanics in an area and properties that require flood insurance negatively affect selling price.
New facts are documented about self-assessed home valuations using household panel data and a near-census of home sale prices. Between 2002 and 2012, homeowners’ display a small positive bias of around 1 per cent in estimating the market value of their homes, although there is considerable dispersion in beliefs and prices. Household characteristics, including age, tenure, and income and local area characteristics, such as unemployment, are associated with differences between beliefs and prices. The extent of overvaluation is positively associated with household spending, leverage and risky-asset holdings. Over the housing cycle, homeowner valuations appear less volatile than sale prices and are backward-looking; homeowners also learn from past ‘errors’. These facts support recent literature on the importance of belief formation for household decision-making.
The advancement of computational software within the last decade has facilitated enhanced uptake of mass appraisal methodologies by the valuation and prediction accuracy in computer-assisted mass appraisal community for price modelling, estimation and tribunal defence. Applying a sample of 2694 residential properties, this paper assesses and analyses a number of geostatistical approaches relative to an artificial neural network (ANN) model and the traditional linear hedonic pricing model for mass appraisal valuation accuracy and price estimation purposes. The findings demonstrate that the geostatistical localised regression approach is superior in terms of model explanation, reliability and accuracy. ANNs can be shown to perform very well in terms of predictive power, and therefore valuation accuracy, outperforming the traditional multiple regression analysis (MRA) and approaching the performance of spatially weighted regression approaches. However, ANNs retain a ‘black box’ architecture that limits their usefulness to practitioners in the field. In relation to cost-effectiveness and user-friendly applicability for the valuation community, the MRA approach outperforms the ‘black box’ nature of the ANN technique, with the geographically weighted regression approach providing the best balance of outright performance and transparency of methodology. It is this spatially weighted approach utilising absolute location which appears to represent the way forward in developing the practice of mass appraisal.
Housebuilding firms vary across the world in size and in the scope of their activities. This variety may seem surprising in an industry with open technologies and ease of entry. While market and technological factors may go some way to explain such differences, much of the causes of variation lie in dissimilarities in regulatory and institutional frameworks. These themes are explored through a comparative analysis of the structure of the residential development industry in Australia, the UK and the USA and in analysis of firm size hierarchies. The firm concentration ratio is much higher in the UK than the other two countries and the reasons may lie in the geography of the country but also in the peculiarities of its planning system.
Market value predictions for residential properties are important for investment decisions and the risk management of households, banks, and real estate developers. The increased access to market data has spurred the development and application of Automated Valuation Models (AVMs), which can provide appraisals at low cost. We discuss the stages involved when developing an AVM. By reflecting on our experience with md*immo, an AVM from Berlin, Germany, our paper contributes to an area that has not received much attention in the academic literature. In addition to discussing the main stages of AVM development, we examine empirically
the statistical model development and validation step. We �find that automated outlier removal is important and that a log model performs best, but only if it accounts for the retransformation problem and heteroscedasticity.
This paper investigates the relationship between the list and sale price of residential properties over the housing cycle. In down or normal markets the list price generally exceeds the sales price; however, when the housing market is strong, homes sell for more than their list price. This observation is not consistent with the assumptions made in the standard model of home sellers’ search behavior. We consider alternative models. In one, sellers set list prices based on their expectations of future changes in sales prices and the arrival rate of buyers; however, demand shocks occur. This model partially explains our data from the Belfast, U.K. housing market, but it fails to predict the list to sales price ratio during a sustained housing boom. We next describe a model where sellers’ endogenously select their search mechanism depending on the strength of the housing market. We find support for the conjecture that sellers switch to an auction-like model during housing booms. There also is evidence that during a downturn in the market, sellers’ list prices are sticky.
This paper presents new empirical evidence that internal movement - selling one home and buying another - by existing homeowners within a metropolitan housing market is especially volatile and the main driver of fluctuations in transaction volume over the housing market cycle. We develop a dynamic search equilibrium model that shows that the strong pro-cyclicality of internal movement is driven by the cost of simultaneously holding two homes, which varies endogenously over the cycle. We estimate the model using data on prices, volume, time-on-market, and internal moves drawn from Los Angeles from 1988-2008 and use the fitted model to show that frictions related to the joint buyer-seller problem: (i) substantially amplify booms and busts in the housing market, (ii) create counter-cyclical build-ups of mismatch of existing owners with their homes, and (iii) generate externalities that induce significant welfare loss and excess price volatility.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
This paper examines the effects of seller uncertainty over their home value on the housing market. Using evidence from a new dataset on home listings and transactions, I first show that sellers do not have full information about current period demand conditions for their homes. I incorporate this type of uncertainty into a dynamic search model of the home selling problem with Bayesian learning. Simulations of the estimated model show that information frictions help explain short-run persistence in price appreciation rates and a positive (negative) correlation between price changes and sales volume (time on market).
This study compares owner's estimates of house value and professional appraisals for a sample of owner occupied single family and multifamily units in St. Louis. It confirms the findings of an earlier study by Kish and Lansing that: (1) the errors of estimate are quite large for individual properties, but, (2) the errors are largely offsetting for reasonably sized samples. In addition, the analysis indicates errors of estimate are systematically related to the socioeconomic characteristics of the owner-occupants and that knowledge of these biases can be used to improve the accuracy of both the individual and aggregate estimates of market value.
Purpose
– The traditional models of real estate market have several sources of imprecision, such as transitions between submarkets, generating difficulties in property valuation. The purpose of this paper is to examine an alternative to improve mass appraisal models, using fuzzy rules.
Design/methodology/approach
– Fuzzy rule‐based systems (FRBS) are able to generate flexible systems and may be useful in considering vagueness or imprecision presents in real estate market. An application to the housing market of Porto Alegre (Brazil), with more than 30,000 apartments, transacted in 1998‐2001, illustrates the fuzzy system, comparing with traditional hedonic regression model.
Findings
– The results have indicated the potential of fuzzy rules to use in mass appraisal.
Originality/value
– This paper presents a procedure to develop mass appraisal models using FRBS.
The primary objective of this study is to apply hedonic regression techniques to an office market to identify and quantify the significant contribution of the different attributes to office rents. This technique is widely used in the analysis of housing markets but an extensive literature review reveals little application in commercial property markets. The study analyses a sample of 477 asking rents, together with a series of locational and physical attributes, for the City of Glasgow. The results explain approximately 60 per cent of variation in rents across the city, emphasizing the importance of age and location as principal determinants of rents.
A hedonic equation considers OLS models with independently and identically distributed errors. However, quality of property and location tend to exhibit highly auto- regressive correlation due to spatial dependence and heterogeneity. A modié cation is made to multiple regression analysis based on the land-rent concept so that the modié ed hedonic house price function can be determined. This paper develops a stochastic approach which is able to correct autocorrelation bias in the hedonic function. Others tend to model the spatial autocorre- lation through the error terms, while this study models it through the constant term. The sample was orientated by location to reè ect the neighbourhood effects. Thus the neighbourhood effects can be separated from the random disturbance. Our model, using data from Hong Kong, incorporates adjustments reè ecting net è oor area ratio, age, è oor level, views, transport accessi- bility and amenities such as availability of recreational facilities. The stochastic model is a more è exible application of hedonic price regressions, making it an appealing alternative to spatial problems.
Explains that bias in human problem-solving behaviour may sometimes conflict with the assumptions that underlie normative models of valuation. Possibilities include faulty perceptions of the task, routinized valuation procedures that rely heavily on pending sale price-knowledge and reliance on innate and distorting problem-solving heuristics. Describes an experiment conducted with appraisers in the USA and valuers in the UK to investigate the impact of price knowledge on the process of valuation and the search and selection of comparable sales. Concludes that both appraisers and valuers can be subject to price-knowledge bias, reflected in the choice of the less than “best” comparables and in the actual value estimate. Discusses how cultural differences in the appraisal and valuation processes, especially in the transparency of comparables adjustments, help to explain these effects.
A weakness in most discussions about definitions of market value is that no explicit criteria for judging whether a definition is good or bad are presented. In this article three criteria are analysed: the definition should have a clear meaning, the definition should be such that it is possible to know, at least approximately, what the market value is, and finally that the definition should lead to a concept that is relevant for actors on the market. These three criteria are then used to analyse three questions: “Should the definition include a reference to prudent and knowledgeable actors?”, “Should the definition refer to willing buyers and sellers?” and finally, “How shall we interpret the concept of expected or probable price?” The answer to the first and second question is no. The answer to the third question is that a frequency interpretation of probability should be rejected in valuation contexts, in favour of an interpretation where probability is identified with a rationally justified degree of confidence.
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Reviews the literature on the meaning of "home" published between 1974 and 1989 in disciplines investigating person–environment relationships. The meaning of home has been defined mostly for traditional households living in single-family detached houses, although there is growing concern among recent studies about nontraditional populations and settings. The role of material aspects of housing and of societal forces in the production and reproduction of the meaning of home has been neglected in the literature. Exemplary studies from other areas of housing research that emphasize these macrosocietal forces are presented. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Understanding the spatial variation in housing prices plays a crucial role in topics ranging from the cost of living to quality-of-life indices to studies of public goods and household mobility. Yet analysts have not reached a consensus on the best source of such data, variously using self-reported values from the census, transactions values, tax assessments, and rental values. Additionally, while most studies use micro-level data, some have used summary statistics such as the median housing value.Assessing neighborhood price indices in Los Angeles, we find that indices based on transactions prices are highly correlated with indices based on self-reported values, but the former are better correlated with public goods. Moreover, rental values have a higher correlation with public goods and income levels than either asset-value measure. Finally, indices based on median values are poorly correlated with the other indices, public goods, and income.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
This paper measures the effects of real estate brokerage services provided to sellers, other than MLS listings, on the terms and timing of home sales. It is not obvious that sellers benefit from those services. On the one hand, brokers offer potentially useful knowledge and expertise. On the other hand, because the relationship between the homeowner and the broker resembles a classical principal-agent problem, the broker may not deploy services in ways that promote the seller’s interests. Yet as long as valuable MLS listings are bundled with brokerage services, homeowners may use brokers even if the agency costs exceed the benefits of brokers’ knowledge and expertise. Thus, quantification of the net value of brokerage services other than MLS listings bears directly on the recent policy debate over the desirability unbundling of MLS listings. We estimate the effect of a seller’s decision to use a broker on list prices, selling prices, and speed of sale for a real estate market with an unusual and critical characteristic: it has a single open-access listing service that is used by essentially all sellers, regardless of whether they employ brokers. Our central finding is that, when listings are not tied to brokerage services, a seller’s use of a broker reduces the selling price of the typical home by 5.9 to 7.7 percent, which indicates that agency costs exceed the advantages of brokers’ knowledge and expertise by a wide margin.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
The endowment effect describes the fact that people demand much more to give up an object than they are willing to spend to acquire it. The existence of this effect has been documented in numerous experiments. We attempt to explain this effect by showing that evolution favors individuals whose preferences embody an endowment effect. The reason is that an endowment effect improves one's bargaining position in bilateral trades. We show that for a general class of evolutionary processes strictly positive endowment effects will survive in the long run.
Housing prices vary widely from market to market in the United States. The purpose of this study is to (1) construct new place-to-place indexes of the price of housing, using the 1990 Census, and (2) analyze the determinants of housing prices, with a particular focus on the supply side determinants—regulatory and natural constraint—as well as the usual demand determinants.
This article specifies and estimates a computationally tractable stationary equilibrium model of the housing market. The model is rich and incorporates many of its unique features: buyers’ and sellers’ simultaneous search behavior, heterogeneity in their motivation to trade, transaction costs, a trading mechanism with posting prices and bargaining, and the availability of an exogenous advertising technology that induces endogenous matching. Estimation uses Maximum Likelihood methods and Multiple Listing Services data. The estimated model is used to simulate housing market outcomes when (a) the amount of information displayed on housing listings increases and (b) real estate agent’s commission rates change.
An analysis of the demand for air quality in four MSAs in the United States is presented using the American Housing Survey data from 1974-1991, the Decennial U.S. Censuses, and the EPA Aerometric Information Retrieval System. The marginal prices of air quality are obtained from parameter estimates for the pollution variables in a hedonic house price model, and the marginal willingness to pay (inverse demand) equations for air quality are estimated using these prices. In two of the four (inverse) demand for air quality equations, the own-good coefficient is negative and significant, while the income coefficient is positive and significant.
This study presents a latent variable framework to provide consistent and efficient estimates of market values of amenities. A model for property values of residential housing using different indicators for neighborhood quality and property value is estimated using data from the U.S. American Housing Survey. The estimated effect of neighborhood quality on property values is positive and more significant compared to the estimates obtained by ordinary least squares and instrumental variable methods. Variances of errors of measurement and variances of the latent structures are shown to be positive and significant without imposing nonnegativity restrictions.
Identifying the local interactions between housing prices and population migration is complicated by their simultaneous and spatially interdependent relationship. Higher housing prices may repel households and push them into neighboring areas, suggesting that separately identifying interactions within versus across local neighborhoods is important. Aggregate data and standard econometric models are unable to address the multiple identification problems that may arise from the simultaneity, spatial interaction, and unobserved spatial autocorrelation. Such problems can generate biased estimates that run counter to economic theory. Using Michigan census tract-level data, we estimate a spatial simultaneous equations model that jointly considers population change and housing values, while also explicitly modeling interactions within neighborhoods, spatial interactions across neighborhoods, and controlling for unobserved spatial correlations. After controlling for simultaneity and spatial autocorrelation, the results show that neighborhoods are likely to experience an increase in their housing values if they gain population and they are more likely to lose population if they experience an increase in housing values. Our results are consistent with theory and underscore the importance of accounting for spatial interdependencies between population change and housing values.
Numerous econometric models have used various estimates of housing value as dependent variables. The three most common measures, in order of descending popularity, have been homeowner estimates, sales price, and assessed value. Each of these measures has limitations. The use of sales price can cause sample selection bias, while owner and tax assessor estimates are subject to measurement error. This study investigates the magnitude of the selection bias associated with sales price samples, and whether the errors in owner and assessor estimates are systematically related to independent variables typically included in estimated equations. Our most important conclusion is that the use of owner estimates may cause bias in the estimated coefficients on many independent variables.
Housing prices differ substantially among metropolitan areas. The rent and house price indexes used here measure this variation among 54 metropolitan areas. A model of metropolitan housing price determination is presented and used to identify the sources of intermetropolitan price variation. Reduced-form equations explain close to 90% of the variation in rental prices and close to 60% of the variation in house prices.