Vol. 87 (2025)
Original Articles - Appraisal and Rural Economics

Using a spatial econometric approach to identify the main determinants and spillover effects of residential property prices in La Spezia (Italy)

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  • Laura Giuffrida,
  • Giuseppe Cucuzza,
  • Daniela Tavano,
  • Francesca Salvo,
  • Giovanni Signorello,
  • Maria De Salvo
Laura Giuffrida
Department of Agriculture, Food and Environment, University of Catania, Italy
Giuseppe Cucuzza
Department of Agriculture, Food and Environment, University of Catania, Italy
Daniela Tavano
Department of Environmental Engineering, University of Calabria, Italy
Francesca Salvo
Department of Environmental Engineering, University of Calabria, Italy
Giovanni Signorello
Department of Agriculture, Food and Environment, University of Catania, Italy
Maria De Salvo
Department of Veterinary Sciences, University of Messina, Italy
DOI: https://doi.org/10.36253/aestim-17865

Published 2025-10-29

Keywords

  • Spatial autocorrelation,
  • Econometric spatial models,
  • Real estate market analysis

Copyright (c) 2025 Laura Giuffrida, Giuseppe Cucuzza, Daniela Tavano, Francesca Salvo, Giovanni Signorello, Maria De Salvo

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

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Abstract

We employ a spatial econometric approach to investigate the factors influencing residential property prices in La Spezia province (Italy). Unlike traditional hedonic models, which often overlook spatial dependencies, our methodology explicitly accounts for spatial autocorrelation, thereby yielding more robust and accurate estimates. Diagnostic spatial tests reveal significant spatial dependence in both property prices and context variables. To address this, we adopt the Spatial Durbin Error Model (SDEM), using a first-order Queen contiguity weight matrix. This model not only enhances explanatory power but also improves predictive accuracy. By incorporating spatial effects, the SDEM enables the disentanglement of direct and spillover influences, offering a more comprehensive understanding of the determinants of property prices. The findings demonstrate the importance of spatially-aware models not only in the formulation of effective housing policies and urban development strategies but also in appraisal practices, where they improve the accuracy of real estate valuation.

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References

  1. Aladwan, Z., & Ahamad Mohd, S. S. (2019). The hedonic pricing model for real property valuation via GIS-A review. Civil and Environmental Engineering Reports, 29(3), 34–47. DOI: https://doi.org/10.2478/ceer-2019-0022
  2. Algieri, B. (2013). House price determinants: fundamentals and underlying factors. Comparative Economic Studies, 55, 315–341. DOI: https://doi.org/10.1057/ces.2013.3
  3. American Institute of Real Estate Appraisers. (1952). The Appraisal of Real Estate. The Institute.
  4. Anderson, S. T., & West, S. E. (2006). Open space, residential property values, and spatial context. Regional Science and Urban Economics, 36(6), 773–789. DOI: https://doi.org/10.1016/j.regsciurbeco.2006.03.007
  5. Anselin, L. (2022). Spatial econometrics. In Rey, S. J., & Franklin, R. (Eds.). Handbook of spatial analysis in the social sciences, pp. 101-122. Cheltenham, Edward Elgar Publishing.
  6. Arcuri, N., De Ruggiero, M., Salvo, F., & Zinno, R. (2020). Automated valuation methods through the cost approach in a BIM and GIS integration framework for smart city appraisals. Sustainability, 12(18), 7546. DOI: https://doi.org/10.3390/su12187546
  7. Aziz, A., Anwar, M. M., Abdo, H. G., Almohamad, H., Al Dughairi, A. A., & Al-Mutiry, M. (2023). Proximity to neighborhood services and property values in urban area: An evaluation through the hedonic pricing model. Land, 12(4), 859. DOI: https://doi.org/10.3390/land12040859
  8. Barreca, A., Curto, R., & Rolando, D. (2018). Housing vulnerability and property prices: spatial analyses in the Turin real estate market. Sustainability, 10(9), 3068. DOI: https://doi.org/10.3390/su10093068
  9. Belke, A., & Keil, J. (2018). Fundamental determinants of real estate prices: a panel study of German regions. International Advances in Economic Research, 24, 25–45. DOI: https://doi.org/10.1007/s11294-018-9671-2
  10. Bernknopf, R., Gillen, K., Wachter, S., & Wein, A. (2010). Using econometrics and geographic information systems for property valuation: a spatial hedonic pricing model. In Linne, M., & Thompson, M. (Eds.). Visual valuation: implementing valuation modeling and geographic information solutions. Chicago, Appraisal Institute, 265–300.
  11. Bocci, C., Ferretti, C., & Lattarulo, P. (2019). Spatial interactions in property tax policies among Italian municipalities. Papers in Regional Science, 98(1), 371–392. DOI: https://doi.org/10.1111/pirs.12341
  12. Bolitzer, B., & Netusil, N. R. (2000). The impact of open spaces on property values in Portland, Oregon. Journal of Environmental Management, 59(3), 185–193. DOI: https://doi.org/10.1006/jema.2000.0351
  13. Caliman, T., & Di Bella, E. (2011). Spatial autoregressive models for house price dynamics in Italy. Economics Bulletin, 31(2), 1837–1855. DOI: https://doi.org/10.2139/ssrn.1645156
  14. Can, A. (1990). The measurement of neighborhood dynamics in urban house prices. Economic Geography, 66(3), 254–272. DOI: https://doi.org/10.2307/143400
  15. Case, B., Clapp, J., Dubin, R., & Rodriguez, M. (2004). Modeling spatial and temporal house price patterns: a comparison of four models. The Journal of Real Estate Finance and Economics, 29, 167–191. DOI: https://doi.org/10.1023/B:REAL.0000035309.60607.53
  16. Chang, L. C., & Lin, H. Y. (2012). The impact of neighborhood characteristics on housing prices-an application of hierarchical linear modeling. International Journal of Management and Sustainability, 1(2), 31. DOI: https://doi.org/10.18488/journal.11/2012.1.2/11.2.31.44
  17. Chau, K. W., & Chin, T. L. (2003). A critical review of literature on the hedonic price model. International Journal for Housing Science and its applications, 27(2), 145–165.
  18. Chen, Y., Jones, C. A., Dunse, N. A., Li, E., & Liu, Y. (2023). Housing prices and the characteristics of nearby green space: Does landscape pattern index matter? evidence from metropolitan area. Land, 12(2), 496. DOI: https://doi.org/10.3390/land12020496
  19. Cipollini, A., & Parla, F. (2020). Housing market shocks in Italy: a GVAR approach. Journal of Housing Economics, 50, 101707. DOI: https://doi.org/10.1016/j.jhe.2020.101707
  20. Copiello, S. (2020). Spatial dependence of housing values in Northeastern Italy. Cities, 96, 102444. DOI: https://doi.org/10.1016/j.cities.2019.102444
  21. Crompton, J. L. (2001). The impact of parks on property values: a review of the empirical evidence. Journal of Leisure Research, 33(1), 1–31. DOI: https://doi.org/10.1080/00222216.2001.11949928
  22. Cunha, A. M., & Lobão, J. (2021). The determinants of real estate prices in a European context: a four-level analysis. Journal of European Real Estate Research, 14(3), 331–348. DOI: https://doi.org/10.1108/JERER-10-2020-0053
  23. De Noni, I., Ghidoni, A., Menzel, F., Bahrs, E., & Corsi, S. (2019). Exploring drivers of farmland value and growth in Italy and Germany at regional level. Aestimum, 74, 77–99. DOI: https://doi.org/10.13128/aestim-7381
  24. De Ruggiero, M., & Salvo, F. (2011). Misure di similarità negli adjustment grid methods. Aestimum, 58, 47–58. DOI: https://doi.org/10.13128/Aestimum-9561
  25. De Toro, P., Nocca, F., Renna, A., & Sepe, L. (2020). Real estate market dynamics in the city of Naples: an integration of a multi-criteria decision analysis and geographical information system. Sustainability, 12(3), 1211. DOI: https://doi.org/10.3390/su12031211
  26. Drukker, D. M., Prucha, I. R., & Raciborski, R. (2013). Maximum likelihood and generalized spatial two-stage least-squares estimators for a spatial-autoregressive model with spatial-autoregressive disturbances. The Stata Journal, 13(2), 221–241. DOI: https://doi.org/10.1177/1536867X1301300201
  27. Durst, N. J., Sullivan, E., & Jochem, W. C. (2024). The spatial and social correlates of neighborhood morphology: Evidence from building footprints in five US metropolitan areas. Plos one, 19(4), e0299713. DOI: https://doi.org/10.1371/journal.pone.0299713
  28. Elhorst, J. P. (2010). Applied spatial econometrics: raising the bar. Spatial Economic Analysis, 5(1), 9–28. DOI: https://doi.org/10.1080/17421770903541772
  29. Elhorst, J. P. (2014). Spatial econometrics: from cross-sectional data to spatial panels. Heidelberg, Springer. DOI: https://doi.org/10.1007/978-3-642-40340-8
  30. European Construction Sector Observatory (2019). Housing affordability and sustainability in the EU. Analytical report. Available at: https://single-market-economy.ec.europa.eu/sectors/construction/observatory/analytical-reports_en (Accessed 10 September 2024).
  31. Fingleton, B. (2006). A cross‐sectional analysis of residential property prices: the effects of income, commuting, schooling, the housing stock and spatial interaction in the English regions. Papers in Regional Science, 85(3), 339–361. DOI: https://doi.org/10.1111/j.1435-5957.2006.00089.x
  32. Ganduri, R., Xiao, S. C., & Xiao, S. W. (2023). Tracing the source of liquidity for distressed housing markets. Real Estate Economics, 51(2), 408–440. DOI: https://doi.org/10.1111/1540-6229.12388
  33. Giuffrida, L., De Salvo, M., Manarin, A., Vettoretto, D., & Tempesta, T. (2023). Exploring farmland price determinants in Northern Italy using a spatial regression analysis. Aestimum, 83, 3–20. DOI: https://doi.org/10.36253/aestim-14986
  34. Guan, C., & Peiser, R. B. (2018). Accessibility, urban form, and property value. Journal of Transport and Land Use, 11(1), 1057–1080. DOI: https://doi.org/10.5198/jtlu.2018.1318
  35. Herath, S., & Maier, G. (2010). The hedonic price method in real estate and housing market research: a review of the literature. WU Vienna University of Economics and Business. SRE - Discussion Papers No. 2010/03
  36. Hidano, N., Hoshino, T., & Sugiura, A. (2015). The effect of seismic hazard risk information on property prices: evidence from a spatial regression discontinuity design. Regional Science and Urban Economics, 53, 113–122. DOI: https://doi.org/10.1016/j.regsciurbeco.2015.05.005
  37. Jin, S., Zheng, H., Marantz, N., & Roy, A. (2024). Understanding the effects of socioeconomic factors on housing price appreciation using explainable AI. Applied Geography, 169, 103339. DOI: https://doi.org/10.1016/j.apgeog.2024.103339
  38. Kishor, N. K. (2022). Comovements and spillovers in international commercial and residential real estate markets. Journal of European Real Estate Research, 15(3), 311–331. DOI: https://doi.org/10.1108/JERER-07-2021-0037
  39. Lee, C. C., Liang, C. M., Yeh, W. C., & Yu, Z. (2022). The impact of urban renewal on neighboring housing prices: An application of hierarchical linear modeling. International Journal of Strategic Property Management, 26(1), 11­23. DOI: https://doi.org/10.3846/ijspm.2022.15971
  40. LeSage, J. P., & Pace, R. K. (2014). The biggest myth in spatial econometrics. Econometrics, 2(4), 217–249. DOI: https://doi.org/10.3390/econometrics2040217
  41. Li, J., Zheng, L., Liu, C., & Shen, Z. (2021). Information spillover effects of real estate markets: evidence from ten metropolitan cities in China. Journal of Risk and Financial Management, 14(6), 244. DOI: https://doi.org/10.3390/jrfm14060244
  42. Lindsey, G., Payton, S., Man, J., & Ottensmann, J. (2003). Public choices and property values: evidence from Greenways in Indianapolis. Indianapolis: Center for Urban Policy and the Environment. 12 p.
  43. Lo, D., Chau, K. W., Wong, S. K., McCord, M., & Haran, M. (2022). Factors affecting spatial autocorrelation in residential property prices. Land, 11(6), 931. DOI: https://doi.org/10.3390/land11060931
  44. Locurcio, M., Morano, P., Tajani, F., & Di Liddo, F. (2020). An innovative GIS-based territorial information tool for the evaluation of corporate properties: An application to the Italian context. Sustainability, 12(14), 5836. DOI: https://doi.org/10.3390/su12145836
  45. Mahan, B. L., Polasky, S., & Adams, R. M. (2000). Valuing urban wetlands: a property price approach. Land Economics, 76(1), 100–113. DOI: https://doi.org/10.2307/3147260
  46. Manski, C. F. (1993). Identification of endogenous social effects: the reflection problem. The Review of Economic Studies, 60(3), 531–542. DOI: https://doi.org/10.2307/2298123
  47. Marinković, S., Džunić, M., & Marjanović, I. (2024). Determinants of housing prices: Serbian Cities’ perspective. Journal of Housing and the Built Environment, 39(3), 1601–1626. DOI: https://doi.org/10.1007/s10901-024-10134-5
  48. Morena, M., Cia, G., Baiardi, L., & Rodríguez Rojas, J. S. (2021). Residential property behavior forecasting in the metropolitan city of Milan: socio-economic characteristics as drivers of residential market value trends. Sustainability, 13(7), 3612. DOI: https://doi.org/10.3390/su13073612
  49. Musa, U. S. M. A. N., & Yusoff, W. Z. W. (2015). Impact of neighborhood characteristics on residential property values: a critical review of literature. International Review of Social Sciences, 3(4), 147–155.
  50. Musa, U., & Yusoff, W. Z. W. (2017). The influence of housing components on prices of residential houses: a review of literature. The Social Sciences, 12(4), 625–632.
  51. Osland, L., Östh, J., & Nordvik, V. (2022). House price valuation of environmental amenities: an application of GIS‐derived data. Regional Science Policy & Practice, 14(4), 939–959. DOI: https://doi.org/10.1111/rsp3.12382
  52. Paraschiv, C., & Chenavaz, R. (2011). Sellers’ and buyers’ reference point dynamics in the housing market. Housing Studies, 26(03), 329–352. DOI: https://doi.org/10.1080/02673037.2011.542095
  53. Poulhes, M. (2018). From Latin Quarter to Montmartre: Investigating Parisian Real Estate Prices. Annals of Economics and Statistics/Annales d’Économie et de Statistique, 130, 39–68. DOI: https://doi.org/10.15609/annaeconstat2009.130.0039
  54. Riccioli, F., Fratini, R., & Boncinelli, F. (2021). The impacts in real estate of landscape values: evidence from Tuscany (Italy). Sustainability, 13(4), 2236. DOI: https://doi.org/10.3390/su13042236
  55. Rosato, P., Breil, M., Giupponi, C., & Berto, R. (2017). Assessing the impact of urban improvement on housing values: a hedonic pricing and multi-attribute analysis model for the historic centre of Venice. Buildings, 7(4), 112. DOI: https://doi.org/10.3390/buildings7040112
  56. Rosiers, F. D., Lagana, A., & Theriault, M. (2001). Size and proximity effects of primary schools on surrounding house values. Journal of Property Research, 18(2), 149–168. DOI: https://doi.org/10.1080/09599910110039905
  57. Ruggeri, A. G., Gabrielli, L., Scarpa, M., & Marella, G. (2023). What is the impact of the energy class on market value assessments of residential buildings? An analysis throughout Northern Italy based on extensive data mining and artificial intelligence. Buildings, 13(12), 2994. DOI: https://doi.org/10.3390/buildings13122994
  58. Salvo, F., De Ruggiero, M., & Tavano, D. (2022). Social variables and real estate values: the case study of the City of Cosenza. In Napoli, G., Mondini, G., Oppio, A., Rosato, P., & Barbaro, S. (Eds.). Values, Cities and Migrations: Real Estate Market and Social System in a Multi-cultural City. pp. 173-186. Cham, Springer International Publishing. DOI: https://doi.org/10.1007/978-3-031-16926-7_13
  59. Salvo, F., Tavano, D., & De Ruggiero, M. (2021). Hedonic price of the built-up area appraisal in the market comparison approach. In Bevilacqua, C., Calabrò, F., & Della Spina, L. (Eds.). New Metropolitan Perspectives: Knowledge Dynamics and Innovation-driven Policies Towards Urban and Regional Transition Volume 2. pp. 696-704. Cham, Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-48279-4_65
  60. Sani, A., Mohammed, M. I., & Usman, H. (2023). Locational, neighbourhood and physical characteristics of residential rental properties: a review. Journal of Commerce, Management, and Tourism Studies, 2(3), 143–154. DOI: https://doi.org/10.58881/jcmts.v2i3.121
  61. Seo, K., Golub, A., & Kuby, M. (2014). Combined impacts of highways and light rail transit on residential property values: a spatial hedonic price model for Phoenix, Arizona. Journal of Transport Geography, 41, 53-62. DOI: https://doi.org/10.1016/j.jtrangeo.2014.08.003
  62. Seo, Y. (2020). Varying effects of urban tree canopies on residential property values across neighborhoods. Sustainability, 12(10), 4331. DOI: https://doi.org/10.3390/su12104331
  63. Sica, F., Tajani, F., & Cerullo, G. (2025). An evaluation model for an optimal decarbonisation process in the built environment. Built Environment Project and Asset Management, 15(1), 51–66. DOI: https://doi.org/10.1108/BEPAM-05-2024-0126
  64. Simonotti, M. (2006). Metodi di stima immobiliare. Applicazione degli standard internazionali. Palermo, Dario Flaccovio.
  65. Stamou, M., Mimis, A., & Rovolis, A. (2017). House price determinants in Athens: a spatial econometric approach. Journal of Property Research, 34(4), 269–284. DOI: https://doi.org/10.1080/09599916.2017.1400575
  66. Tajani, F., Manganelli, B., Cerullo, G., Morano, P., & Morente, M. A. (2023, June). The student housing as a catalyst for virtuous processes of “win-win” revitalization of property assets in disuse. In Gervasi, O., Murgante, B., Rocha, A. M. A. C., Garau, C., Scorza, F., Karaca, Y., Torre, C. M. (Eds.). Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14109. pp. 387-400. Cham, Springer. DOI: https://doi.org/10.1007/978-3-031-37120-2_25
  67. Trojanek, R., Gluszak, M., & Tanas, J. (2018). The effect of urban green spaces on house prices in Warsaw. International Journal of Strategic Property Management, 22(5), 358–371. DOI: https://doi.org/10.3846/ijspm.2018.5220
  68. Vergos, K. P., & Zhi, H. (2018). Is it a curse or a blessing to live near rich neighbors? Spatial analysis and spillover effects of house prices in Beijing. Spatial Analysis and Spillover Effects of House Prices in Beijing (November 22, 2018). DOI: https://doi.org/10.2139/ssrn.3289359
  69. Wang, Y., Wang, S., Li, G., Zhang, H., Jin, L., Su, Y., & Wu, K. (2017). Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique. Applied Geography, 79, 26–36. DOI: https://doi.org/10.1016/j.apgeog.2016.12.003
  70. Wong, S. K., Chau, K. W., Yau, Y., & Cheung, A. K. C. (2011). Property price gradients: the vertical dimension. Journal of Housing and the Built Environment, 26, 33–45. DOI: https://doi.org/10.1007/s10901-010-9203-8
  71. Zhang, Y., Zhang, D., & Miller, E. J. (2021). Spatial autoregressive analysis and modeling of housing prices in city of Toronto. Journal of Urban Planning and Development, 147(1), 05021003. DOI: https://doi.org/10.1061/(ASCE)UP.1943-5444.0000651
  72. Zihannudin, N. Z., Maimun, N. H. A., & Ibrahim, N. L. (2021). Brownfield sites and property market sensitivity. Planning Malaysia, 19(2), 121–130. DOI: https://doi.org/10.21837/pm.v19i16.957
  73. Zoppi, C., Argiolas, M., & Lai, S. (2015). Factors influencing the value of houses: estimates for the city of Cagliari, Italy. Land Use Policy, 42, 367–380. DOI: https://doi.org/10.1016/j.landusepol.2014.08.012

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