Examining The Effect of Spatial Variables on Housing Price (Case Study: Valiasr Neighborhood, Shiraz)

Document Type : Original Article

Authors

1 Department of Urban Planning & Design, Faculty of Art and Architecture, University of Shiraz, Shiraz, Iran

2 Assistant Professor, Department of Urban Planning & Design, Faculty of Art and Architecture, University of Shiraz, Shiraz, Iran

10.22124/upk.2025.29689.2010

Abstract

Introduction: 
Housing is a critical decision and a major investment for households, characterized by heterogeneity and the complex interaction of multiple intrinsic characteristics. Its value arises from a set of structural, spatial, and environmental characteristics. In developing countries such as Iran, rapid urbanization and policy challenges exacerbate housing issues and make it essential to understand the determinants of housing prices. This study addresses a research gap in Shiraz by quantitatively analyzing the impact of spatial variables on housing prices in Valiasr neighborhood using the Hedonic Pricing Method (HPM). The research classifies the variables into internal (physical-structural) and external (accessibility and environmental-spatial) indicators to build a reliable pricing model.
Methodology: This applied research has a descriptive-analytical and correlational approach. Data were collected from 117 housing transactions in Valiasr neighborhood from April to October 2022. Primary data collection included structured questionnaires and semi-structured interviews with real estate agents. From an initial set of 38 spatial variables, 27 variables were selected for the final analysis after applying scientific criteria.

Dependent Variable: Price per square meter (MPrice and its log form LnMPrice).
Independent Variables: Physical-structural indicators (e.g., usable area, year of construction, type of structure), accessibility (e.g., distance to metro, bus, parks), and environmental-spatial indicators (e.g., road width, security).

Spatial analysis was performed using GIS and Open Street Map (OSM)-based network analysis to calculate walkable distances to urban services. Statistical analysis and HPM estimation were performed in SPSS 26 software, and both linear and semi-logarithmic forms were tested through Enter and Stepwise regression methods.
Results: The comparison of the estimated models revealed that the semi-logarithmic model developed using the stepwise method (Model 4) was the most effective in explaining housing price variations. 
Out of the 27 independent variables, only 6 were statistically significant in the best-fitted model (Model 4). The final estimated semi-logarithmic regression equation is as follows:
Ln(MPrice) = -6.954 + 0.018*(Build_age) + 0.012*(Width) + 0.155*(Security) + 0.002*(N_Area) - 0.649*(H_Typo) - 0.079*(Structure)
The results indicate:

Positive and Direct Effects: The variables floor area ratio(N_Area), year of construction (Build_age), road width (Width), and security (Security) have a positive and significant relationship with housing prices. This means newer buildings, larger usable areas, wider roads, and more security (e.g., alley guards) increase property values.
Negative Effects: The variables housing type (H_Typo) and structure type (Structure) have a negative and significant effect. Specifically, apartments (H_Typo) and concrete structures (Structure) were associated with lower prices per square meter compared to other types.

Discussion: The results highlight the important role of spatial variables in housing prices. The superiority of the semi-logarithmic model is consistent with some previous studies but contradicts others, highlighting the suitability of the model for any context. The positive effects of usable area and building age reflect preferences for spacious, modern houses. Road width and security emphasize the value of spatial and environmental quality. The negative associations with apartment type and concrete structures may reflect local market preferences or perceptions of quality and safety. The integration of HPM with GIS-based spatial analysis effectively decomposes housing value, with stepwise regression effectively identifying key predictors.
Conclusion: This study provides a measurable model that identifies key spatial drivers of housing prices in Valiasr, Shiraz. The most influential factor is the building's construction year. The findings are valuable for:

Urban Planners: Informing evidence-based policies and development priorities.
Investors: Identifying opportunities and understanding market dynamics.
Policymakers: Designing targeted interventions and effective housing regulations.

Future research should expand the geographical scope, consider more detailed environmental variables, and apply spatial econometric techniques to address potential autocorrelation.

Keywords

Main Subjects

References

Ahmadi, H. (1994). Housing planning and urban planning: From conflict to coordination. In Proceedings of the First Seminar on Housing Development Policies in Iran (Vol. 2). Ministry of Housing and Urban Development, Tehran.
Amid, H. (2010). Amid Persian dictionary (1st ed., A. Alizadeh, Ed.). Rah-e Roshd Publications, Tehran. URL: https://www.gisoom.com/book/1662275//
Amirnejad H, Nabizadeh Zolpirani M, Heydari Kamalabadi R. (2016). The Impact of Rasht Eynak Lagoon on Housing Price of the Region by Using Hedonic Pricing Method. IUESA 2016; 4(16), 37-53.(In Persian) URL: http://iueam.ir/article-1-544-en.html
Arnott, R. (2015). Housing economics. Doi:‏ https://doi.org/10.1016/B978-0-08-097086-8.71049-X
Asgari, a., & Ghaderi, j. (2002). Determination of housing price in urban areas of iran by using hedonic price method (hpm). Journal of sustainable growth and development (the economic research), 1(4), 91-108. (In Persian). URL: https://sid.ir/paper/86377/en
Asgharzad, E., Hojabr kiani, K., Emami Meybodi, A., & Asgari, F. (2020). Investigating the impact of air pollution on housing prices in Tehran using the Hadonic model. Urban Economics, 5(2), 71-84. (In Persian)  Doi: https://doi.org/10.22108/ue.2022.128249.1186
Azar, A. (2025). Measuring the Spatial Quality of Housing Using Multi-Criteria Decision-Making Models (MCDM); A Case Study of Tabriz Metropolis. Journal of Geography and Regional Development, 23(1), 97-126. Doi: https://doi.org/10.22067/jgrd.2025.87018.1406  
Bitter, C., Mulligan, G., & Dall’erba, S. (2007). Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method. Journal of Geographical Systems, 9(1), 7-27. Doi: https://doi.org/10.1007/s10109-006-0028-7
Cebula, R. J. (2009). The hedonic pricing model applied to the housing market of the city of Savannah and its Savannah historic landmark district. Review of Regional Studies, 39(1), 9-22.‏ 10.52324/001c.8197
Emami maboodi, A. , Azami, A. and Haghdoost, E. (2010). Environmental Effective Factors on Houses Prices in Tehran: Hedonic Pricing Approach. Journal of Economic Research (Tahghighat- E- Eghtesadi), 44(2).
Ghafari, F., & Ovasi, T. (2012). Estimation of the Housing DemandFunction for the city of Qom, using the Hedonic Price Pattern .Applied Economics ,11(3) ,27-48. (In Persian) URL: https://sanad.iau.ir/en/Journal/jae/Article/804178
Ghamkhar, E., Keshvardoust, A.R., Hasanpour, R., & Mosapour meiandehi, P. (2014). Study the qualitative and quantitative indicators and estimating needed home in rasht city until 1400. Journal of the studies of human settlements planning (journal of geographical landscape), 8(25), 95-112. (In Persian). URL: https://sid.ir/paper/176068/en
Ghorbani, S, & afgheh, S.M. (2017). Forecasting the House Price for Ahvaz City: the Comparison of the Hedonic And Artificial Neural Network Models. Journal Of Urban Economics and Management, 5(3( , 29-44. (In Persian). URL: https://sid.ir/paper/240237/en
Golkar, k. (2001). Components of Urban Design Quality. Soffeh, 11(32), 38-65. (In Persian). URL: https://sid.ir/paper/94234/en
Harvey. David (2009) Social Justices and the City, The University of Georgia Press, Second Edition, London
Hekmatnia, H., & Mousavi, M. (2006). Application of Models in Geography With Emphasis on Urban And Regional Planning. Elm-e Novin Publications.
Heyman, A. V., & Sommervoll, D. E. (2019). House Prices And Relative location. Cities, 95(10), 23-73. Doi: http://dx.doi.org/10.1016/j.cities.2019.06.004
Hjalmarsson, E., & Österholm, P. (2020). Heterogeneity in Households’ Expectations of Housing Prices–Evidence From Micro Data. Journal of Housing Economics, 50(10), 17-31.‏ Doi: https://doi.org/10.1016/j.jhe.2020.101731
Jim, C. Y., & Chen, W. Y. (2007). Consumption Preferences And Environmental Externalities: A Hedonic Analysis of The Housing Market in Guangzhou. Geoforum, 38(2), 414-431. Doi: http://dx.doi.org/10.1016/j.geoforum.2006.10.002
Kain John. F & Quigley (1975); The value of housing attributes, In Housing markets and racial discrimination: A Microeconomic analysis, Natianal Bureau of Economic Research, 190-230. URL: https://econpapers.repec.org/RePEc:nbr:nberbk:kain75-1
Kazakeviciute, A., & Banyte, J. (2012). The Relationship of Consumers ‘Perceived Hedonic Value And Behavior. Engineering Economics, 23(5), 532-540. Doi:‏ http://dx.doi.org/10.5755/j01.ee.23.5.1975
Khalili Araghi M, Nobahar E. (2012). Predicting Housing Prices for the City of Tabriz: Application of the Hedonic Pricing and Artificial Neural Network Models. Qjerp ; 19(60) :113-138 (In Persian) URL: http://qjerp.ir/article-1-189-en.html
Kokabi, A., Pourjafar, M., & Taghvai, A. (2005). Urban Quality of Life Planning in Urban Centers: Definitions and Indicators. Journal of Urban Studies, 12, 6-13. (In Persian) URL: http://noo.rs/bm0Y9
Liao, W. C., & Wang, X. (2012). Hedonic House Prices And Spatial Quantile Regression. Journal of Housing Economics, 21(1), 16-27. URL:‏ http://www.sciencedirect.com/science/article/pii/S1051137711000568
Lancaster, K. J. (1966). A New Approach to Consumer Theory. Journal of Political Economy, 74(2), 132–157 Doi: https://doi.org/10.1086/259131.
Maleki, S. (2003). The Role of Social Indicators in Housing Development Planning in Ilam City. Housing and Revolution Quarterly, 104(4), 60-75. (In Persian) URL: https://sid.ir/paper/437205/fa
Movahed, M., Sheikhi, H. (2021). Study the Effect of Public Transportation Expansion on Housing Price Using the Hedonic Model (A Case Study of Kermanshah), Amayesh Journal, 13(51), 159-178. (In Persian) URL: https://www.magiran.com/p2257198
Opoku, R.A., Abdul-Muhmin, A.G. (2010). Housingpreferences and Attribute Importance Among low-Income Consumers in Saudi Arabia, Habitat International 34, pp 219–227. Doi: http://dx.doi.org/10.1016/j.habitatint.2009.09.006
Pogodzinski, J. M., & Sass, T. R. (1991). Zoning and Hedonic Housing Price Models. Journal of Housing Economics, 1(3), 271-292. Doi: https://doi.org/10.1016/S1051-1377(05)80037-3
Potrawa, T. & Tetereva, A. (2022). How Much Is the View from the Window Worth? Machine Learning-Driven Hedonic Pricing Model of the Real Estate Market. Journal of Business Research, 144, 50-65.‏ Doi: https://doi.org/10.1016/j.jbusres.2022.01.027
Pourmohammadi, M. (2017). Planning for housing. Secound Edition with Additions. Samt Publications, Tehran.
Pourmohammadi, M.R., & Asadi, A. (2014). An Evaluation of Public Low Income Housing Policies in Iran (The Case of Zanjan City) (In Persian) URL: http://geographical-space.iau-ahar.ac.ir/article-1-982-en.html
Pourmohammadi, M. R., Hakimi, H., & Mirzaie, A. (2018). Studying the Relationship between Building Density and Land Price: Case Study of the Municipal Zone 1 of Tabriz Metropolis. Geography and Urban Space Development, 4(2), 169-188. (In Persian) Doi: https://doi.org/10.22067/gusd.v4i2.56319
Pröbstl-Haider, U. (2015) Cultural Ecosystem Services And Their Effects on Human Health And Well-Being – A cross-Disciplinary Methodological Review. Journal of Outdoor Recreation and Tourism, 10, 1-13. Doi: https://doi.org/10.1016/j.jort.2015.07.004
Rahdari, M., & Leylian, N. (2012). Estimate of Demand Function of Housing, Using the Hedonic Price Model (1378-88) Case Study: Kerman. Journal of Economics and Modelling, 3(10), 237-258. (In Persian) URL: https://ecoj.sbu.ac.ir/article_53518.html
Rahimi Kakejob, A., & Eisalou, A.A., & Mohammadian Mosammam, h., & Rahmati, A. (2013). An Investigation Into Effective Factors Deciding Urban Housing Prices Via Hedonic Pricing Model (The Case Of District 2 Of The City Of Sanandaj). Journal Of Urban Economics And Management, 1(3), 33-43. (In Persian). URL: https://sid.ir/paper/240310/en
Ridker, R. G., & Henning, J. A. (1967). The Determinants of Residential Property Values with Special Reference to Air Pollution. The Review of Economics and Statistics, 49(2), 246–257. Doi: https://doi.org/10.2307/1928231
Rosen, S. (1974). Hedonic Prices And Implicit Markets: Product Differentiation In Pure Competition. Journal of Political Economy, 82(1), 34–55. Doi: https://doi.org/10.1086/260169
Saremi, H., Heydari, M., & Aghaei, F. (2018). Spatial Analysis Of Housing Price Using Geographically Weighted Regression (A Case Study In District 2 of Tehran Metropolitan City, Iran). Urban Economics, 3(2), 19-38. (In Persian) Doi: 10.22108/ue.2018.109447.1056
Sayyami, A. (2007). Estimating the Hedonic Rent Function: A Case Study of Tabriz, Urmia, and Ardabil Cities (Master's thesis). Faculty of Economic Sciences and Finance, University of Mazandaran.
Sedaghati, A., & Nouriyan, D. F. (2016). Utilizing Hedonic Price Method in Valuing Residential Properties Case Study: Baghe Saba Neighborhood in District 7 of Tehran. Geography and Territorial Spatial Arrangement, 6(21), 171-186. (In Persian) Doi: 10.22111/gaij.2016.2841
Teimouri, I., Hakimi, H., & Hossianpour Shad, V. (2016). The Role of Physical and Accessibility Variables in Determination of Housing Price; Case Study Sahand New Town. Journal of Geography and Planning, 20(57), 81-95. (In Persian) https://doi:2 
Tong, S., Wong, N. H., Tan, C. L., Jusuf, S. K., Ignatius, M., & Tan, E. (2017). Impact of Urban Morphology on Microclimate And Thermal Comfort In Northern China. Solar Energy, 155, 212-223.‏ Doi: http://dx.doi.org/10.1016/j.solener.2017.06.027
Van Kamp, I., Leidelmeijer, K., Marsman, G., & De Hollander, A. (2003). Urban Environmental Quality And Human Well-Being: Towards A Conceptual Framework And Demarcation of Concepts; A Literature Study. Landscape and urban planning, 65(1-2), 5-18. Doi: https://doi.org/10.1016/S0169-2046(02)00232-3
Venkatachalam, L. (2004). The Contingent Valuation Method: A Review. Environmental Impact Assessment Review, 24(1), 89-124. Doi: https://doi.org/10.1016/S0195-9255(03)00138-0
Yankaya, U., & Celik, H. M. (2004). Modelling the Impacts of Rail Transit Investment on the Values of Residential Property: A Hedonic Price Approach in the Case of Izmir Subway, Turkey. Report Izmir Institute of Technology, Department of Civil Engineering. Doi: https://doi.org/10.4236/cus.2020.84037
Yasmin, F. (2017). The Impact of View and Accessibility Amenities on High-Rise Residential Properties in the City of Dhaka: A Hedonic Pricing Model (Doctoral dissertation) URL: https://hdl.handle.net/1969.1/165802 
Urban Planning Knowledge
Volume 9, Issue 4
January 2026
Pages 1-19

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  • Receive Date: 03 February 2025
  • Revise Date: 05 August 2025
  • Accept Date: 18 September 2025

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