Skip to main content
SpringerLink
Log in

Estimating Housing Prices: A Proposal with Spatially Correlated Data

  • Published:
International Advances in Economic Research Aims and scope Submit manuscript

Abstract

The price of housing per square meter and the trend observed over the last few years is one of the issues that most concerns Spanish citizens and subsequently their political and economic representatives. However, in spite of the importance of space in the real estate market, official averages do not take into account the spatial correlation of housing prices. In order to solve this handicap, we propose the kriging the mean method to estimate mean housing prices. This method provides the best unbiased linear estimation taking into account spatially correlated data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. The covariance function has been taken from Cressie (1993), p. 14. This function is obtained from an autorregresive first-order process, \( X\left( {{s_i}} \right) = \rho X\left( {{s_{i - 1}}} \right) + \varepsilon \left( {{s_i}} \right)\quad i \geqslant 1 \), where ε(si) are random independent and equally distributed Gaussian variables with null mean and variance σ2 (1−ρ2), independent from X (s i−1). Nevertheless, in this article \( X \equiv \left( {X\left( {{s_1}} \right), \ldots, X\left( {{s_n}} \right)} \right) \) are spatial data in ℝ2 so that the prediction of X(s 0) or \( X\left( {{s_{{\raise0.7ex\hbox{$3$} \!\mathord{\left/{\vphantom {3 2}}\right.}\!\lower0.7ex\hbox{$2$}}}}} \right) \) is so appropriated as the one from X (s n+1).

References

  • Case, B., Clapp, J. M., Dubin, R. A., & Rodríguez, M. (2004). Modeling spatial and temporal house price patterns: a comparison of four models. Journal of Real Estate Finance and Economics, 29(2), 167–191.

    Article  Google Scholar 

  • Clapp, J. M., Kim, H. J., & Gelfand, A. E. (2002). Predicting spatial patterns of house prices using LPR and bayesian smoothing. Real Estate Economics, 30(4), 505–532.

    Article  Google Scholar 

  • Cressie, N. A. C. (1993). Statistics for spatial data. New York: John Wiley & Sons.

    Google Scholar 

  • Din, A., Hoesli, M., & Bender, A. (2001). Environmental variables and real estate prices. Urban Studies, 38(11), 1989–2000.

    Article  Google Scholar 

  • Dubin, R. A. (1998). Predicting house prices using multiple listing data. Journal of Real Estate Finance and Economics, 17(1), 144–164.

    Article  Google Scholar 

  • Gámez, M., Montero, J. M., & García, N. (2000). Kriging methodology for regional economic analysis. International Advances in Economic Research, 6(3), 438–450.

    Article  Google Scholar 

  • Gelfand, A. E., Ecker, M. J., Knight, J. R., & Sirmans, C. F. (2004). The dynamics of location in home price. Journal of Real Estate Finance and Economics, 29(2), 149–166.

    Article  Google Scholar 

  • Grenander, U. (1954). On the estimation of regression coefficients in the case of an autocorrelated disturbance. Annals of Mathematical Statistics, 25, 252–272.

    Article  Google Scholar 

  • Grenander, U., & Rosenblatt, M. (1984). Statistical analysis of stationary time series (2nd ed.). New York: Chelsea Publishing Co.

    Google Scholar 

  • Han, S. S. (2004). Spatial structure of residential property-value distribution in Beijing and Jakarta. Environment and Planning A, 36(7), 1259–1283.

    Article  Google Scholar 

  • Haining, R. P. (1988). Estimating spatial means with an application to remotely sensed data, Communications in Statistics. Theory and Methods, 17, 573–597.

    Article  Google Scholar 

  • Larraz-Iribas, B., & Alfaro-Navarro, J. L. (2008). Asymmetric behaviour of spanish regional house prices. International Advances in Economic Research, 14(4), 407–421.

    Article  Google Scholar 

  • Ministerio de Vivienda (2008a). Notas Metodológicas. http://www.mviv.es

  • Ministerio de Vivienda (2008b). Estadística de Precios de Vivienda. http://www.mviv.es

  • Montero-Lorenzo, J. M., & Larraz-Iribas, B. (2006a). Estimación espacial del precio de la vivienda mediante métodos de krigeado. Revista de Estadística Española, 48(162), 201–240.

    Google Scholar 

  • Montero-Lorenzo, J. M., & Larraz-Iribas, B. (2006b). Estimating housing prices: kriging the mean-research note. International Advances in Economic Research, 12(2), 419.

    Article  Google Scholar 

  • Montero-Lorenzo, J. M., & Larraz-Iribas, B. (2008). Introducción a la Geoestadística Lineal. A Coruña: Editorial Netbiblo.

    Google Scholar 

  • Rodríguez, J. (2007). 1997–2007: el auge inmobiliario más largo, gracias al euro. Clm.economía, 11, 9–36.

    Google Scholar 

  • Yiu, C. Y., & Tam, C. S. (2007). Housing price gradient with two workplaces—An empirical study in Hong Kong. Regional Science and Urban Economics, 37(3), 413–429.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Facultad de Ciencias Jurídicas y Sociales, Universidad de Castilla-La Mancha, Cobertizo de San Pedro Mártir, s/, 45071, Toledo, Spain

    Jose M. Montero & Beatriz Larraz

Authors
  1. Jose M. Montero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Beatriz Larraz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Beatriz Larraz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Montero, J.M., Larraz, B. Estimating Housing Prices: A Proposal with Spatially Correlated Data. Int Adv Econ Res 16, 39–51 (2010). https://doi.org/10.1007/s11294-009-9244-5

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11294-009-9244-5

Keywords

JEL

Search

Navigation