The inflated valuation problem in Valencia, Spain, and implications for firm size

    Natividad Guadalajara Affiliation
    ; Miguel A. López Affiliation


Home purchase-sale prices have been widely modeled by several authors. Nonetheless, other values exist, such as home mortgage appraisal values, used by financial institutions, which have played a key role in the recent financial crisis. This article attempts to model the appraisal price of one m2 of residential properties obtained by 31 appraisal companies in Valencia (Spain). Mortgage appraisal values of 17 007 residential properties were used for this purpose. Spatial autocorrelation was detected in both the data and residuals of the ordinary regression model, which justified using spatial regression models. Of the four employed models, the error model offered the best results. Significant differences were found among appraisal companies, which varied as much as 83% for some. Generally speaking, small appraisal companies obtained higher over-valuation percentages, which confirms their situation of weakness. The fact that over-valuations exist in mortgage securities is a high risk for a stable financial system.

Keyword : firm size, housing, mortgage, overvaluation, spatial

How to Cite
Guadalajara, N., & López, M. A. (2018). The inflated valuation problem in Valencia, Spain, and implications for firm size. International Journal of Strategic Property Management, 22(4), 300-313.
Published in Issue
Aug 10, 2018
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Affuso, E., Cummings, J. R., & Le, H. (2017). Wireless towers and home values: an alternative valuation approach using a spatial econometric analysis. Journal of Real Estate Finance and Economics, 56(4), 653-676.

Agnello, L., & Schuknecht, L. (2011). Booms and busts in housing markets: determinants and implications. Journal of Housing Economics, 20, 171-190.

Akin, O., Montalvo, J. G., Garcia Villar, J., Peydro, J. L., & Raya, J. M. (2014). The real estate and credit bubble: evidence from Spain. SERIEs, 5(2-3), 223-243.

Amidu, A. R., & Aluko, B. T. (2007). Client influence in residential property valuations: an empirical study. Property Management, 25(5), 447-461.

Anselin, L. (1988). Spatial econometrics: methods and models. Kluwer Academic Publishers, Dordrecht, The Netherlands.

Anselin, L. (1998). Exploratory spatial data analysis in a geo-computational environment. Geocomputation, a Primer (pp. 77-94). Wiley, New York.

Anselin, L., & Rey, S. J. (2014). Modern spatial econometrics in practice: a guide to GeoDa, GeoDaSpace and PySAL. GeoDa Press LLC.

Aspachs-Bracons, O., & Rabanal, P. (2010). The drivers of housing cycles in Spain. SERIEs, 1(1-2), 101-130.

Baffour Awuah, K. G., & Gyamfi-Yeboah, F. (2017). The role of task complexity in valuation errors analysis in a developing real estate market. Journal of Property Research, 34(1), 54-76.

Belsky, E., Can, A., & Megbolugbe, I. (1998). A Primer on geographic information systems in mortgage finance. Journal of Housing Research, 9(1), 5-31.

Borst, R., & McCluskey, W. (2007). Comparative evaluation of the comparative sales method with geostatistical valuation models. Pacific Rim Property Research Journal, 13(1), 106-129.

Bowcock, P. (2015). A discussion paper on valuations for mortgage and the level of house prices. International Journal of Housing Markets and Analysis, 8(1), 27-35.

Brasington, D. M. (2004). House prices and the structure of local government: an application of spatial statistics. Journal of Real Estate Finance and Economics, 29(2), 211-231.

Can, A. (1990). The measurement of neighborhood dynamics in urban house prices. Economic geography, 66(3), 254-272.

Can, A. (1992). Specification and estimation of hedonic housing price models. Regional science and urban economics, 22(3), 453-474.

Cerruti, E., Dagher, J., & Dell’Ariccia, G. (2017). Housing finance and real-estate booms: a cross-country perspective. Journal of Housing Economics, 38, 1-13.

Chasco, C., & Le Gallo, J. (2013). The Impact of objective and subjective measures of air quality and noise on house prices: a multilevel approach for Downtown Madrid. Economic Geography, 89(2), 127-148.

Chegut, A. M., Eichholtz, P. M. A., & Rodrigues, P. J. M. (2015). Spatial dependence in international office markets. Journal of Real Estate Finance and Economics, 51(2), 317-350.

Chinloy, P., Cho, M., & Megbolugbe, I. F. (1997). Appraisals, transaction incentives and smoothing. Journal of Real Estate Finance and Economics, 14, 89-111.

Cho, M., & Megbolugbe, I. F. (1996). An empirical analysis of property appraisal and mortgage redlining. Journal of Real Estate Finance and Economics, 13, 45-55.

Crosby, N., Lizieri, C., & McAllister, P. (2010). Means, motive and opportunity? Disentangling client influence on performance measurement appraisals. Journal of Property Research, 27(2), 181-201.

Dubin, R. (1998). Estimation of regression coefficients in the presence of spatially autocorrelated error terms. The Review of Economics and Statistics, 70(3), 466-474.

Gallimore, P., & Wolverton, M. (2000). The objective in valuation: a study of the influence of client feedback. Journal of Property Research, 17(1), 47-57.

García, J., & Raya, J. M. (2011). Price and income elasticities of demand for housing characteristics in the city of Barcelona. Regional Studies, 45(5), 597-608.

García Montalvo, J. (2009). Financiación inmobiliaria, burbuja crediticia y crisis financiera: lecciones a partir de la recesión de 2008–2009. Papeles de Economía Española, 122, 66-87.

Hordijk, A., & Van de Ridder, W. (2005). Valuation model uniformity and consistency in real estate indices: the case of the Netherlands. Journal of Property Investment & Finance, 23(2), 165-181.

Jimeno, J. F., & Santos, T. (2014). The crisis of the Spanish economy. SERIEs, 5, 125-141.

Kelejian, H. H., & Prucha, I. R. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model. International Economic Review, 40(2), 509-533.

Kelejian, H. H., & Prucha, I. R. (2010). Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics, 157(1), 53-67.

Klamer, P., Bakker, C., & Gruis, V. (2017). Research bias in judgement bias studies – a systematic review of valuation judgement literature. Journal of Property Research, 34(4), 285-304.

Krause, A. L., & Bitter, C. (2012). Spatial econometrics, land values and sustainability: trends in real estate valuation research. Cities, 29, S19-S25.

Lacour-Little, M., & Malpezzi, S. (2003). Appraisal quality and residential mortgage default: evidence from Alaska. Journal of Real Estate Finance and Economics, 27(2), 211-233.

Levy, D., & Schuck, E. (1999). The influence of clients on valuations. Journal of Property Investment & Finance, 17(4), 380-400.

Levy, D., & Schuck, E. (2005). The influence of clients on valuations: the clients’ perspective. Journal of Property Investment & Finance, 23(2), 182-201.

Ministerial Order ECO/805/2003, BOE 27 March 2003.

Moran, P. (1950). A test for the serial independence of residuals. Biometrika, 37(1/2), 178-181.

Montalvo, J. G., & Raya, J. M. (2017). Constraints on LTV as a macroprudential tool: a precautionary tale. Department of Economics and Business, Universitat Pompeu Fabra. Forthcoming in Oxford Economic papers.

Royal Decree 775/1997, BOE 30 May 1997.

Salon, D., Wu, J., & Shewmake, S. (2014). Impact of bus rapid transit and metro rail on property values in Guangzhou, China. Transportation Research Record. Journal of the Transportation Research Board, (2452), 36-45.

Tidwell, O. A., & Gallimore, P. (2014). The influence of a decision support tool on real estate valuations. Journal of Property Research, 31(1), 45-63.

Zhang, R., Du, Q., Geng, J., Liu, B., & Huang, Y. (2015). An improved spatial error model for the mass appraisal of commercial real estate based on spatial analysis: Shenzhen as a case study. Habitat International, 46, 196-205.