Multicriteria analysis and genetic algorithms for mass appraisals in the Italian property market

International Journal of Housing Markets and Analysis

Volumn 11, Issue 2, 2018, Pages 229-262

  Morano, Pierluigi ^a   Tajani, Francesco ^a   Locurcio, Marco ^b  

^a POLITECNICO DI BARI   (Italy)

^b SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Genetic algorithm; Goal programming; Marginal utility; Mass appraisal; Property prices; Utility additive

Indexed keywords

EID: 85040786137     PISSN: 17538270     EISSN: 17538289     Source Type: Journal    
DOI: 10.1108/IJHMA-04-2017-0034     Document Type: Article

References (117)

1. Adair, A., Mcgreal, S., Smyth, A., Cooper, J. and Ryley, T. (2000), “House prices and accessibility: the testing of relationships within the Belfast urban area”, Housing Studies, Vol. 15 No. 5, pp. 699-716.
2. Adolphson, D.L., Cornia, G.C. and Walters, L.C. (1989), “Railroad property valuation using data envelopment analysis”, Interfaces, Vol. 19 No. 3, pp. 18-26.
3. Anselin, L. and Getis, A. (1992), “Spatial statistical analysis and geographic information systems”, The Annals of Regional Science, Vol. 26 No. 1, pp. 19-33.
4. Antipov, E.A. and Pokryshevskaya, E.B. (2012), “Mass appraisal of residential apartments: an application of random Forest for valuation and a CART-based approach for model diagnostics”, Expert Systems with Applications, Vol. 39 No. 2, pp. 1772-1778.
5. Aouni, B. and Martel, J.M. (2004), “Property assessment through an imprecise goal programming model”, Information Systems and Operational Research, Vol. 42 No. 3, pp. 189-200.
6. Bagnoli, C. and Smith, H.C. (1998), “The theory of fuzzy logic and its application to real estate valuation”, Journal of Real Estate Research, Vol. 16 No. 2, pp. 169-200.
7. Blomquist, G. and Worley, L. (1981), “Hedonic prices, demand for urban housing amenities and benefit estimates”, Journal of Urban Economics, Vol. 9 No. 2, pp. 212-221.
8. Bonissone, P.P. and Cheetham, W. (1997), “Financial applications of fuzzy case-based reasoning to residential property valuation”, Fuzz-IEEE, Vol. 1, pp. 37-44.
9. Borsa Immobiliare di Napoli (2015), “Listino Ufficiale: Valori correnti del mercato immobiliare della città di Napoli”, Camera di Commercio, Industria, Artigianato ed Agricoltura di Napoli.
10. Borst, R. (1991), “Artificial neural networks: the next modelling/calibration technology for the assessment community?”, Property Tax Journal, Vol. 10 No. 1, pp. 69-94.
11. Bourassa, S.C., Cantoni, E. and Hoesli, M. (2007), “Spatial dependence, housing submarkets, and house price prediction”, The Journal of Real Estate Finance and Economics, Vol. 35 No. 2, pp. 143-160.
12. Bourassa, S.C., Cantoni, E. and Hoesli, M. (2010), “Predicting house prices with spatial dependence: a comparison of alternative methods”, Journal of Real Estate Research, Vol. 32 No. 2, pp. 139-159.
13. Bourassa, S.C., Hoesli, M. and Peng, V.S. (2003), “Do housing submarkets really matter?”, Journal of Housing Economics, Vol. 12 No. 1, pp. 12-28.
14. Bowes, D.R. and Ihlanfeldt, K.R. (2001), “Identifying the impacts of rail transit stations on residential property values”, Journal of Urban Economics, Vol. 50 No. 1, pp. 1-25.
15. Brander, L.M. and Koetse, M.J. (2011), “The value of urban open space: meta-analyses of contingent valuation and hedonic pricing results”, Journal of Environmental Management, Vol. 92 No. 10, pp. 2763-2773.
16. Byrne, P. (1994), “Fuzzy analysis: a vague way of dealing with uncertainty in real estate analysis”, Journal of Property Valuation & Investment, Vol. 13 No. 3, pp. 22-41.
17. Cechin, A., Souto, A. and Aurelio, M. (2000), “Real estate value at Porto Alegre City using artificial neural networks”, Sixth Brazilian Symposium on Neural Networks Proceedings, Porto Alegre City, 237-242.
18. Chang, Y. and Ko, T. (2013), “An interactive dynamic multi-objective programming model to support better land use planning”, Land Use Policy, Vol. 36, pp. 13-22.
19. Chen, N.K., Chen, S.S. and Chou, Y.H. (2010), “House prices, collateral constraint, and the asymmetric effect on consumption”, Journal of Housing Economics, Vol. 19 No. 1, pp. 26-37.
20. Collins, A. and Evans, A. (1994), “Artificial neural networks: an application to residential valuation in the UK”, Journal of Property Valuation and Investment, Vol. 11 No. 2, pp. 195-204.
21. Colwell, P.F., Gujral, S.S. and Coley, C. (1985), “The impact of shopping center on the value of surrounding properties”, Real Estate Issues, Vol. 10 No. 1, pp. 35-39.
22. Cressie, N.A.C. (1993), Statistics for Spatial Data, Wiley & Sons, New York, NY.
23. Crompton, J.L. (2001), “The impact of parks on property values: a review of the empirical evidence”, Journal of Leisure Research, Vol. 33, pp. 1-31.
24. Despotis, D.K. and Zopounidis, C. (1993), “Building additive utilities in the presence of nonmonotonicpreference”, in Pardalos, P.M., Siskos, Y. and Zopounidis, C. (Eds), Advances Inmulticriteria Analysis, Kluwer Academic Publisher, Dordrecht.
25. Des Rosiers, F., Lagana, A., Thériault, M. and Beaudoin, M. (1996), “Shopping centers and house values: an empirical investigation”, Journal of Property Valuation and Investment, Vol. 14 No. 4, pp. 41-63.
26. Ding, C., Simons, R. and Baku, E. (2000), “The effect of residential investment on nearby property values: evidence from cleveland, Ohio”, The Journal of Real Estate Research, Vol. 19 No. 1, pp. 23-48.
27. Do, A.Q. and Grudnitski, G. (1993), “A neural network analysis of the effect of age on housing values”, Journal of Real Estate Research, Vol. 8 No. 2, pp. 253-264.
28. Dubin, R.A. (1992), “Spatial autocorrelation and neighbourhood quality”, Regional Science and Urban Economics, Vol. 22 No. 3, pp. 433-452.
29. Elbourne, A. (2008), “The UK housing market and the monetary policy transmission mechanism: an SVAR approach”, Journal of Housing Economics, Vol. 17 No. 1, pp. 65-87.
30. Emerson, F. (1972), “Valuation of residential amenities: an econometric approach”, The Appraisal Journal, Vol. 40 No. 2, pp. 268-278.
31. Espey, M. and Lopez, H. (2000), “The impact of airport noise and proximity on residential property values”, Growth and Change, Vol. 31 No. 3, pp. 408-419.
32. EstellitaLins, M.P., De Lyra Novaes, L.F. and LoureiroLegey, L.F. (2005), “Real estate appraisal: a double perspective data envelopment analysis approach”, Annals of Operations Research, Vol. 138 No. 1, pp. 79-96.
33. Figueira, J.R., Greco, S. and Słowiński, R. (2009), “Building a set of additive value functions representing a reference preorder and intensities of preference: GRIP method”, European Journal of Operational Research, Vol. 195 No. 2, pp. 460-486.
34. Fletcher, M., Gallimore, P. and Mangan, J. (2000), “The modelling of housing submarkets”, Journal of Property Investment & Finance, Vol. 18 No. 4, pp. 473-487.
35. Gallego, J. (2004), “La inteligencia artificial aplicada a la valoración de inmuebles: un ejemplo paravalorar Madrid”, Revista CT/Catastro, Vol. 50, pp. 51-67.
36. Ge, X.J., Runeson, G. and Lam, K.C. (2003), “Forecasting Hong Kong housing prices: an artificialneural network approach”, Proceeding International Conference on Methodologies in Housing Research.
37. Gelfand, A.E., Ghosh, S., Knight, J.R. and Sirmans, C.F. (1998), “Spatio-temporal modeling of residential sales data”, Journal of Business & Economic Statistics, Vol. 16 No. 3, pp. 312-321.
38. Gibler, K.M., Tyvimaa, T. and Kananen, J. (2014), “The relationship between the determinants of rental housing satisfaction and considering moving in Finland”, Property Management, Vol. 32 No. 2, pp. 104-124.
39. Giustolisi, O. and Savic, D. (2009), “Advances in data-driven analyses and modelling using EPR-MOGA”, Journal of Hydroinformatics, Vol. 11 Nos 3/4, pp. 225-236.
40. Gomes, L.F.A.M. and Rangel, L.A.D. (2009), “Determining the utility functions of criteria used in the evaluation of real estate”, International Journal of Production Economics, Vol. 117 No. 2, pp. 420-426.
41. Goodman, A.C. and Thibodeau, T.G. (2003), “Housing market segmentation and hedonic prediction accuracy”, Journal of Housing Economics, Vol. 12 No. 3, pp. 181-201.
42. Graves, P., Murdoch, J.C., Thayer, M.A. and Waldman, D. (1988), “The robustness of hedonic price estimation: urban air quality”, Land Economics, Vol. 64 No. 3, pp. 220-233.
43. Greco, S., Mousseau, V. and Słowiński, R. (2008), “Ordinal regression revisited: multiple criteria ranking using a set of additive value functions”, European Journal of Operational Research, Vol. 191 No. 2, pp. 416-436.
44. Grether, D.M. and Mieszkowski, P. (1974), “Determinants of real estate values”, Journal of Urban Economics, Vol. 1 No. 2, pp. 127-145.
45. Grieson, R.E. and White, J.R. (1989), “The existence and capitalization of neighbourhood externalities: a reassessment”, Journal of Urban Economics, Vol. 25 No. 1, pp. 68-76.
46. Griffith, D.A. (1993), “Advanced spatial statistics for analyzing and visualizing geo-references data”, International Journal of Geographical Information Systems, Vol. 7 No. 2, pp. 107-124.
47. Guntermann, K.L. and Colwell, P.F. (1983), “Property values and accessibility to primary schools”, Real Estate Appraiser and Analyst, Vol. 49 No. 1, pp. 62-68.
48. Hamzaoi, Y.E. and Hernández, J.A. (2011), “Application of artificial neural networks to predict the selling price in the real estate valuation”, 10th Mexican International Conference on Artificial Intelligence, pp. 175-181.
49. Hardy, M.A. (1993), Regression with Dummy Variables, Sage Publications, California.
50. Harrison, D.T., Smersh, G. and Schwartz, A. (2001), “Environmental determinants of housing prices: the impact of flood zone status”, Journal of Real Estate Research, Vol. 21 Nos 1/2, pp. 3-20.
51. Harvey, J. (1996), Urban Land Economics: The Economics of Real Property Markets, Macmillan, London.
52. Hefferan, M.J. and Boyd, T. (2010), “Property taxation and mass appraisal valuations in Australia-adapting to a new environment”, Property Management, Vol. 28 No. 3, pp. 149-162.
53. Hickman, E.P., Gaines, J.P. and Ingram, F.J. (1984), “The influence of neighbourhood quality on residential values”, Real Estate Appraiser and Analyst, Vol. 50 No. 2, pp. 36-42.
54. Hui, E.C.M., Chau, C.K., Pun, L. and Law, M.Y. (2007), “Measuring the neighboring and environmental effects on residential property value: using spatial weighting matrix”, Building and Environment, Vol. 42 No. 6, pp. 2333-2343.
55. Iacoviello, M. (2004), “Consumption, house prices, and collateral constraints: a structural econometric analysis”, Journal of Housing Economics, Vol. 13 No. 4, pp. 304-320.
56. International Association of Assessing Officers (2013), Standard on Mass Appraisal of Real Property, IAAO, MO.
57. Islam, K.S. and Asami, Y. (2009), “Housing market segmentation: a review”, Review of Urban & Regional Development Studies, Vol. 21 Nos 2/3, pp. 93-109.
58. Islam, K.S. and Asami, Y. (2011), “Addressing structural instability in housing market segmentation of the used houses of Tokyo, Japan”, Procedia – Social and Behavioral Sciences, Vol. 21, pp. 33-42.
59. Jacquet-Lagreze, E. and Siskos, J. (1982), “Assessing a set of additive utility functions formulticriteria decision-making, the UTA method”, European Journal of Operational Research, Vol. 10 No. 2, pp. 151-164.
60. Janssen, C., Söderberg, B. and Zhou, J. (2001), “Robust estimation of hedonic models of price and income for investment property”, Journal of Property Investment & Finance, Vol. 19 No. 4, pp. 342-360.
61. Janssen, C. and Yang, Z. (1999), “Estimating the market value of a proposed townhouse development”, Journal of Property Investment & Finance, Vol. 17 No. 5, pp. 501-516.
62. Jim, C.Y. and Chen, W.Y. (2006), “Impacts of urban environmental elements on residential housing prices in Guangzhou (China)”, Landscape Urban Planning, Vol. 78 No. 4, pp. 422-434.
63. Jolliffe, I.T. (2002), Principal Component Analysis, SpringerSeries in Statistics, Springer-Verlag, New York, NY.
64. Kauko, T., Hooimeijer, P. and Hakfoort, J. (2002), “Capturing housing market segmentation: an alternative approach based on neural network modeling”, Housing Studies, Vol. 17 No. 6, pp. 875-894.
65. Keeney, R.L. and Raiffa, H. (1976), Decisions with Multiple Objectives: Preferences and Valuetradeoffs, John Wiley and Sons, New York.
66. Kettani, O. and Khelifi, K. (2001), “PariTOP: a goal programming-based software for real estate assessment”, European Journal of Operational Research, Vol. 133 No. 2, pp. 362-376.
67. Kettani, O., Oral, M. and Siskos, Y. (1998), “A multiple criteria analysis model for real estate valuation”, Journal of Global Optimization, Vol. 12 No. 2, pp. 197-214.
68. Kim Hin, D.H. and CaleroCuervo, J. (1999), “A cointegration approach to the price dynamics of private housing”, Journal of Property Investment & Finance, Vol. 17 No. 1, pp. 35-60.
69. Kryvobokov, M. (2007), “What location attributes are the most important for market value? Extraction of attributes from regression models”, Property Management, Vol. 25 No. 3, pp. 257-286.
70. Kumar, A. and Chaudhary, A. (2004), Descriptive Statistics (Statistical Methods), Krishna Prakashan Media Ltd, Meerut, pp. 32-36.
71. La Rose, T.A. (1988), “Global response surface analysis used to update appraisals in a computer assisted mass appraisal environment”, Working paper presented at World Congress III of Computer Assisted Valuation and Land Information Systems, Cambridge, MA.
72. Lee, S., Park, I. and Choi, J.K. (2012), “Spatial prediction of ground subsidence susceptibility using an artificial neural network”, Environmental Management, Vol. 49 No. 2, pp. 347-358.
73. Lentz, G. and Wang, K. (1998), “Residential appraisal and the lending process: a survey of issues”, Journal of Real Estate Research, Vol. 15 No. 1, pp. 11-39.
74. Levine, N. (1996), “Spatial statistics and GIS: software tools to quantify spatial patterns”, Journal of the American Planning Association, Vol. 62 No. 3, pp. 381-390.
75. Lewis, B.D. (2003), “Property tax in Indonesia: measuring and explaining administrative (under-) performance”, Public Administration and Development, Vol. 23 No. 3, p. 227.
76. Liu, J., Zhang, X. and Wu, W. (2006), “Application of fuzzy neural network for real estate prediction”, Lecture Notes in Computer Science, Vol. 39 No. 73, pp. 1187-1191.
77. Lopez-Mosquera, N. and Sanchez, M. (2011), “Emotional and satisfaction benefits to visitors as explanatory factors in the monetary valuation of environmental goods: an application to Periurban green spaces”, Land Use Policy, Vol. 28 No. 1, pp. 151-166.
78. Lutzenhiser, M. and Netusil, N.R. (2001), “The effect of open spaces on a home’s sale price”, Contemporary Economic Policy, Vol. 19 No. 3, pp. 291-298.
79. McCluskey, W., Deddis, W., McBurney, R.D., Mannis, A. and Borst, R. (1997), “Interactive application of computer assisted mass appraisal and geographic information systems”, Journal of Property Valuation and Investment, Vol. 15 No. 5, pp. 448-465.
80. McCluskey, W.J., Deddis, W.G., Lamont, I.G. and Borst, R.A. (2000), “The application of surface generated interpolation models for the prediction of residential property values”, Journal of Property Investment & Finance, Vol. 18 No. 2, pp. 162-176.
81. McGreal, S., Adair, A., McBurney, D. and Patterson, D. (1998), “Neural networks: the prediction of residential values”, Journal of Property Valuation and Investment, Vol. 16 No. 1, pp. 57-70.
82. Makridakis, S., Wheelwright, S.C. and Hyndman, R.J. (1998), Forecasting: Methods and Applications, 3rd ed., John Wiley & Sons, New York, NY.
83. Malpezzi, S., Chun, G.H. and Green, R.K. (1998), “New place-to-place housing price indexes for US metropolitan areas, and their determinants”, Real Estate Economics, Vol. 26 No. 2, pp. 235-274.
84. Mao, Y. and Wu, W. (2011), “Fuzzy real option evaluation of real estate project based on risk analysis”, Systems Engineering Procedia, Vol. 1, pp. 228-235.
85. Mieszkowski, P. and Saper, A.M. (1978), “An estimate of the effects of airport noise on property values”, Journal of Urban Economy, Vol. 5 No. 4, pp. 425-440.
86. Morancho, A.B. (2003), “An hedonic valuation of urban green areas”, Landscape and Urban Planning, Vol. 66 No. 1, pp. 35-41.
87. Morano, P. and Tajani, F. (2013), “Bare ownership evaluation. Hedonic price model vs. Artificial neural network”, International Journal of Business Intelligence and Data Mining, Vol. 8 No. 4, pp. 340-362.
88. Morano, P., Tajani, F. and Locurcio, M. (2015), “Land use, economic welfare and property values: an analysis of the interdependencies of the real estate market with zonal and socio-economic variables in the municipalities of the region of Puglia (Italy)”, International Journal of Agricultural and Environmental Information Systems, Vol. 6 No. 4, pp. 16-39.
89. Netusil, N.R. (2013), “Urban environmental amenities and property values: does ownership matter?”, Land Use Policy, Vol. 31, pp. 371-377.
90. Odland, J. (1988), “Spatial Autocorrelation”, Scientific Geography, Vol. 9, Sage, Newbury Park, CA.
91. Ord, J.K. and Getis, A. (1995), “Spatial autocorrelation statistics: distribution issues and an application”, Geographical Analysis, Vol. 27 No. 4, pp. 286-306.
92. Pagourtzi, E., Assimakopoulos, V., Hatzichristos, T. and French, N. (2003), “Real estate appraisal: a review of valuation methods”, Journal of Property Investment & Finance, Vol. 21 No. 4, pp. 383-401.
93. Panatier, Y. (1996), Variowin: Software for Spatial Data Analysis in 2D, Springer-Verlag, New York, NY.
94. Potepan, M.J. (1996), “Explaining intermetropolitan variation in housing prices, rents and land prices”, Real Estate Economics, Vol. 24 No. 2, pp. 219-245.
95. PWC and Urban Land Institute (2015), Emerging Trends in Real Estate Europe 2016, PWC and Urban Land Institute, London.
96. Scenari Immobiliari (2016), Italia 3D, Outlook 2015 e tendenze 2016.
97. Selim, H. (2009), “Determinants of house prices in Turkey: Hedonic regression versus artificial neural network”, Expert Systems with Applications, Vol. 36 No. 2, pp. 2843-2852.
98. Sirmans, G.S. and Benjamin, J.D. (1989), “Determining apartment rent: the value of amenities, services and external factors”, Journal of Real Estate Research, Vol. 4 No. 2, pp. 33-43.
99. Siskos, Y. and Yannacopoulos, D. (1985), “UTASTAR: an ordinal regression method for building additive value functions”, Investigaçao Operacional, Vol. 5 No. 1, pp. 39-53.
100. Siskos, Y., Grigoroudis, E. and Matsatsinis, N.F. (2005), “UTA methods”, in Greco, S., Figueira, J. and Ehrgott, M. (Eds), Multiple Criteria Decision Analysis: State of the Art Surveys, Springer, New York, NY.
101. Sivitanides, P., Southard, J., Torto, R.G. and Wheaton, W.C. (2001), “The determinants of appraisal-based capitalization rates”, Real Estate Finance, Vol. 18 No. 2, pp. 27-38.
102. Smith, B.H. (1993), “The effect of ocean and lake Coast amenities on cities”, Journal of Urban Economics, Vol. 33 No. 1, pp. 115-123.
103. Sturges, H.A. (1926), “The choice of a class interval”, Journal of the American Statistical Association, Vol. 21 No. 153, pp. 65-66.
104. Tajani, F., Morano, P., Locurcio, M. and Torre, M.C. (2016), “Data-driven techniques for mass appraisals.Applications to the residential market of the city of Bari (Italy)”, International Journal of Business Intelligence and Data Mining, Vol. 11 No. 2.
105. Taltavull de La Paz, P. (2003), “Determinants of housing prices in Spanish cities”, Journal of Property Investment & Finance, Vol. 21 No. 2, pp. 109-135.
106. Theriault, M. and Des Rosiers, F. (1995), “Combining hedonic modelling, GIS and spatial statistics to analyze residential markets in the Quebec Urban Community”, Proceedings of the Joint European Conference on Geographical Information, EGIS Foundation, The Hague, Vol. 2, pp. 131-136.
107. Tiefelsdorf, M. and Boots, B. (1997), “A note on the extremities of local Moran’s its and their impact on global Moran’s I”, Geographical Analysis, Vol. 29 No. 3, pp. 248-257.
108. Trovato, M.R. (2012), “A fuzzy measure of the ability of a real estate capital to increase in value”, The Real Estate Decision Problem for Ortigia, Aestimum, pp. 697-720.
109. Tsatsaronis, K. and Zhu, H. (2004), “What drives housing price dynamics: cross-country evidence”, BIS Quarterly Review, Vol. 1.
110. Tse, R.Y.C. (1997), “An application of the ARIMA model to real estate prices in hong kong”, Journal of Property Finance, Vol. 8 No. 2, pp. 152-163.
111. Ubs (2016), UBS Global Real Estate Bubble Index: For Housing Markets of Select Cities, Neidhart + Schön, Zürich.
112. Waddell, P., Berry, B.J.L. and Hoch, I. (1993), “Residential property values in a multi-nodal urban area: new evidence on the implicit price of location”, The Journal of Real Estate Finance and Economics, Vol. 7 No. 2, pp. 117-141.
113. Walden, M. (1990), “Magnet schools and the differential impact of school quality on residential property values”, Journal of Real Estate Research, Vol. 5 No. 2, pp. 221-230.
114. Worzala, E., Lenk, M. and Silva, A. (1995), “An exploration of neural networks and its application to real estate valuation”, Journal of Real Estate Research, Vol. 10 No. 2, pp. 185-201.
115. Wyatt, P.J. (1995), “Using a GIS for property valuation”, Journal of Property Valuation & Investment, Vol. 14 No. 1, pp. 67-79.
116. Yicheng, J. and Chuanrui, F. (2005), “Mass appraisal: a tax base appraisal method for ad valorem tax”, The Journal of Assets Appraisal, Vol. 11.
117. Zhang, Z. and Griffith, D. (1993), “Developing user-friendly spatial statistical analysis modules for GIS: an example using ArcView”, Computer, Environment and Urban Systems, Vol. 21 No. 1, pp. 5-29.