Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models

Case, Bradford; Clapp, John M.; Dubin, Robin A.; Rodríguez, Mauricio

doi:10.1023/b:real.0000035309.60607.53

Cited by 158 publications

(120 citation statements)

References 18 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…These are error values comparable to those obtained in the literature using OLS (see Case et al 2004 Almost all the variables are statistically significant and have the expected sign. Given the semi-logarithmic functional form of the estimated hedonic equation (2), the coefficients of the continuous variables represent semi-elasticities, i.e.…”

Section: Empirical Application and Resultssupporting

confidence: 76%

A sight for sore eyes: Assessing the value of view and land use in the housing market

Baranzini

Schaerer

2011

Journal of Housing Economics

View full text Add to dashboard Cite

We apply a hedonic model to the Geneva-Switzerland rental market to assess the value of view from dwellings and of land uses around buildings. Using a geographic information system, we calculate three-dimensional view variables, accessibility and land use variables. To our knowledge, this is the first paper to develop precise view measures at the dwelling level, considering surrounding land uses, in an urban context and with a large sample of 13,000 observations. The results show that view of various environmental amenities and its size has a significant impact on rents. The estimated rent premium for a dwelling located in a neighbourhood with an extended surface of water can be as high as 3 percent, and a view of water-covered area can raise rent up to 57 percent. JEL classification: Q5, R14, R52, R31, D62

show abstract

Section: Empirical Application and Resultssupporting

confidence: 76%

A sight for sore eyes: Assessing the value of view and land use in the housing market

Baranzini

Schaerer

2011

Journal of Housing Economics

View full text Add to dashboard Cite

show abstract

“…This literature mostly focusses on the construction of house price indices or the estimation of marginal valuations of individual property characteristics (Hill et al, 1997;Palmquist, 1980Palmquist, , 1991. Few papers have examined the performance of statistical models when predicting property prices out-of-sample (Anglin and Gencay, 1996;Thibodeau, 2003;Bin, 2004;Case et al, 2004). These papers work with log prices and ignore the retransformation problem.…”

Section: Introductionmentioning

confidence: 99%

Automated valuation modelling: a specification exercise

Schulz

Wersing

Werwatz

2013

Journal of Property Research

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractMarket 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.

show abstract

“…He also finds that the out of sample mean square error is reduced by about 11% when compared to OLS. However, Case et al [2004] find that the semi-parametric model does worse than the model with spatially autocorrelated errors (Dubin [1998]). Clapp rules out any relationship between attribute prices and location by suggesting an equilibrium allocation where arbitrage would wipe out any such relation-ships (Rosen [1974]).…”

Section: Semi-parametric and Nonparametric Techniquesmentioning

confidence: 99%

“…They claim that according to the National Association of Realtors, roughly 80% of home buyers buy homes through Realtors and hence the realtor's categorization of markets into sub-markets should contain all the relevant information. Case et al [2004] form neighborhoods by combining census tracts that have similar attribute prices using a clustering method. As mentioned earlier, the neighborhoods they produce in this manner are almost all geographically contiguous.…”

Section: Sub-markets/stability Of the Housing Price Functionmentioning

confidence: 99%

Machine Learning and the Spatial Structure of House Prices and Housing Returns

et al. 2008

View full text Add to dashboard Cite

Economists do not have reliable measures of current house values, let alone housing returns. This ignorance underlies the illiquidity of mortgage-backed securities, which in turn feeds back to deepen the sub-prime crisis. Using a massive new data tape of housing transactions in L.A., we demonstrate systematic patterns in the error associated with using the ubiquitous repeat sales methodology to understand house values. In all periods, the resulting indices under-predict sales prices of less expensive homes, and over-predict prices of more expensive homes. The recent period has produced errors that are not only unprecedentedly large in absolute value, but highly systematic: after a few years in which the indices under-predicted prices, they now significantly over-predict them. We introduce new machine learning techniques from computer science to correct for prediction errors that have geographic origins. The results are striking. Accounting for geography significantly reduces the extent of the prediction error, removes many of the systematic patterns, and results in far less deterioration in model performance in the recent period.

show abstract

Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models

Cited by 158 publications

References 18 publications

A sight for sore eyes: Assessing the value of view and land use in the housing market

A sight for sore eyes: Assessing the value of view and land use in the housing market

Automated valuation modelling: a specification exercise

Machine Learning and the Spatial Structure of House Prices and Housing Returns

Contact Info

Product

Resources

About