Spatial association in students’ residential apartment property characteristics around a university
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Abstract
This study aimed at examining the presence of spatial association in residential property characteristics. It considered self-contained residential apartments around a public university in Akure, Nigeria. It is a survey research where data was collected through the use of questionnaires administered to the occupants of the self-contained apartment within a 500-meter radius of the ‘Southgate’ axis of the university. Data collected was analyzed with the use of ArcGIS software where spatial regression was carried out as well as the Anselin Local Indicator of Spatial Association for Cluster/outlier analysis. The result of the analysis revealed that some of the property characteristics have multicollinearity which led to dropping some of them that are represented by their collinear attribute. Distance from the university was observed to have an inverse relationship with the rental prices while other property attributes have a positive influence. From the cluster/outlier analysis, it was revealed that there are significant clustering/outlying in the central and northwestern parts of the study area. The property physical attributes such as kitchen quality, bathroom quality, toilet quality, window quality, and wall quality are high in the northwestern parts where there are new and modern designs while the central part of the study area has low quality of these attributes. The results of the analysis show that the clustering/ outlying, otherwise known as spatial association, among the property attributes are not the same across a particular place and the assumption of a uniform spatial association across the area would be misleading. It is therefore recommended that analysis of real estate investment around an academic institution in Nigeria should consider the property characteristics that influence prices and also adopt spatial clustering analysis to know the specific property characteristics that are to be improved upon on the quality that should be provided to have the best prices with reference to locations. The originality of this research comes in the use of a relatively smaller study area in examining spatial associations among property characteristics. It also considers the clustering/outlying analysis of the qualities of the property characteristics with reference to specific locations which would help real estate developers and analysts on the quality of finishing that should be given to each property attribute at different locations around a university campus in Nigeria.
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Agostinelli, F., Luflade, M., & Martellini, P. (2024). On the spatial determinants of educational access (No. w32246). National Bureau of Economic Research.
Aluko, O. (2011). The effects of location and neighbourhood attributes on housing values in metropolitan Lagos. Ethiopian Journal of Environmental Studies and Management, 4(2), 69-82. https:/doi.org/10.4314/ejesm.v4i2.8
Amole, D. (2009). Residential satisfaction in students' housing. Journal of Environmental Psychology, 29(1), 76-85. https://doi.org/10.1016/j.jenvp.2008.05.006
Anselin, L. (1995). Local indicators of spatial association-LISA. Geographical analysis, 27(2), 93-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anthony, O. A. (2012). Examination of the determinants of housing values in urban Ghana and Implications for Policy Makers. Journal of African Real Estate Research, 2(1),58-85.
Barreca, A., Curto, R., & Rolando, D. (2018). Housing vulnerability and property prices: Spatial analyses in the Turin real estate market. Sustainability, 10(9), 3068. https://doi.org/10.3390/su10093068
Basu, S., & Thibodeau, T. G. (1998). Analysis of spatial autocorrelation in house prices. The Journal of Real Estate Finance and Economics, 17, 61-85. https://doi.org/10.1023/A:1007703229507
Bello, V. A., & Binuyo, O. P. (2020). Geospatial Techniques for Rental Value Determinantion. Journal of Propety Research & Construction, 4(1), 26 – 45.
Belsley, D. A. (1991). A guide to using the collinearity diagnostics. Computer Science in Economics and Management, 4(1), 33-50. https://doi.org/10.1007/BF00426854
Budziński, W., Campbell, D., Czajkowski, M., Demšar, U., & Hanley, N. (2016). Using geographically weighted choice models to account for spatial heterogeneity of preferences. Working Paper-Faculty of Economic Sciences, University of Warsaw, (WP17/208).
Burrough, P. A., & McDonnell, R. A. (2015). Principles of geographical information systems. Oxford University Press.
Buys, L., Barnett, K., Miller, E., & Hopkinson, C. (2005). Smart housing and social sustainability: learning from the residents of Queensland's research house. International Journal of Emerging Technologies and Society, 3(1), 44-57.
Can, A. (1990). The measurement of neighborhood dynamics in urban house prices. Economic geography, 66(3), 254-272. https://doi.org/10.2307/143400
Chiwuzie, A., Dabara, D. I., Adenipekun, T. M., Prince, E. M., & Ajiboye, B. O. (2019). Tenant’s demand for structural attributes in residential properties: The case of Ede, Nigeria.
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. https://doi.org/10.1111/1540-6229.00048
Collins, K., Babyak, C., & Molone, J. (2006). Treatment of spatial autocorrelation in geocoded crime data. In Proceedings of the American Statistical Association Section on Survey Research Methods, 2864-2871.
Cox, T., & Hurtubia, R. (2020). Subdividing the sprawl: Endogenous segmentation of housing submarkets in expansion areas of Santiago, Chile. Environment and Planning B: Urban Analytics and City Science, 48(7), 1770-1786. https://doi.org/10.1177/2399808320947728
Dizaj, M., & Khanghahi, T. (2022). Students’ residential preferences: a case study is dormitories of University of Mohaghegh Ardabili. Journal of Asian Architecture and Building Engineering, 21(4), 1348-1363. https://doi.org/10.1080/13467581.2021.1941987
Edwards, S. (2019). Student preferences for accommodation at a Cape Town University: an application of the stated preference approach [Doctoral dissertation, Cape Peninsula University of Technology].
Erath, A., Löchl, M., & Axhausen, K. W. (2009). Graph-theoretical analysis of the Swiss road and railway networks over time. Networks and Spatial Economics, 9, 379-400. https://doi.org/10.1007/s11067-008-9074-7
FUTA Giant strides. (2016). A publication of the Federal University of Technology. Akure.
Ghani, Z. A. (2018). Higher education institutions students housing provision for off–campus living in Malaysia [Doctoral dissertation, Universiti Tun Hussein Onn Malaysia].
Goodman, A. C., & Thibodeau, T. G. (1998). Housing market segmentation. Journal of housing economics, 7(2), 121-143. https://doi.org/10.1006/jhec.1998.0229
Griliches, Z. (1971). Price indexes and quality change. Harvard University Press.
Lancaster, K. (1971). Theories of consumer choice from economics: a critical review. National Science Foundation, Directorate for Research Applications, RANN (Hrsg.), Selected aspect of consumer behavior. A summary from the perspective of different disciplines, 11-31.
Gwamna, E. S., Wan Yusoff, W. Z., & Ismail, M. F. (2015). Determinants of land use and property value. Advanced science letters, 21(5), 1150-1153. https://doi.org/10.1166/asl.2015.6065
Hamid, A. M. I., Sipan, I., & Ismail, S. (2012), Geographic information system and spatial analysis of real estate. Universiti Teknologi Malaysia Press.
Hong, J., & Shen, Q. (2013). Residential density and transportation emissions: Examining the connection by addressing spatial autocorrelation and self-selection. Transportation Research Part D: Transport and Environment, 22, 75-79. https://doi.org/10.1016/j.trd.2013.03.006
Jiboye, A. D. (2014). Significance of house-type as a determinant of residential quality in Osogbo, Southwest Nigeria. Frontiers of Architectural Research, 3(1), 20-27. https://doi.org/10.1016/j.foar.2013.11.006
Khozaei, F., Hassan, A. S., Al Kodmany, K., & Aarab, Y. (2014). Examination of student housing preferences, their similarities and differences. Facilities, 32(11/12), 709-722. https://doi.org/10.1108/F-08-2012-0061
Kim, B., & Kim, T. (2016). A study on estimation of land value using spatial statistics: Focusing on real transaction land prices in Korea. Sustainability, 8(3), 203. https://doi.org/10.3390/su8030203
Kim, D., & Shin, Y. H. (2016). Spatial autocorrelation potentially indicates the degree of changes in the predictive power of environmental factors for plant diversity. Ecological indicators, 60, 1130-1141. https://doi.org/10.1016/j.ecolind.2015.09.021
Koster, H. R., & Rouwendal, J. (2012). The impact of mixed land use on residential property values. Journal of regional science, 52(5), 733-761. https://doi.org/10.1111/j.1467-9787.2012.00776.x
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. https://doi.org/10.3390/land11060931
Li, C., Wu, K., & Gao, X. (2020). Manufacturing industry agglomeration and spatial clustering: Evidence from Hebei Province, China. Environment, Development and Sustainability, 22(4), 2941-2965. https://doi.org/10.1007/s10668-019-00328-1
Lin, W. S., Tou, J. C., Lin, S. Y., & Yeh, M. Y. (2014). Effects of socioeconomic factors on regional housing prices in the USA. International Journal of Housing Markets and Analysis, 7(1), 30-41. https://doi.org/10.1108/IJHMA-11-2012-0056
Mei, D., Xiu, C., Feng, X., & Wei, Y. (2019). Study of the school–residence spatial relationship and the characteristics of travel-to-school distance in Shenyang. Sustainability, 11(16), 4432. https://doi.org/10.3390/su11164432
Morenikeji, W., Umaru, E., Pai, H., Jiya, S., Idowu, O., & Adeleye, B. M. (2017). Spatial analysis of housing quality in Nigeria. International Journal of Sustainable Built Environment, 6(2), 309-316. https://doi.org/10.1016/j.ijsbe.2017.03.008
Najib, N., & Abidin, N. (2011). Student residential satisfaction in research universities. Journal of Facilities management, 9(3), 200-212. https://doi.org/10.1108/14725961111148108
Nijënstein, S., Haans, A., Kemperman, A. D., & Borgers, A. W. (2015). Beyond demographics: human value orientation as a predictor of heterogeneity in student housing preferences. Journal of Housing and the Built Environment, 30, 199-217. https://doi.org/10.1007/s10901-014-9402-9
Noraini, M., Sanusi, S., Elham, J., Sukor, Z., & Halim, K. (2017). Factors affecting production of biogas from organic solid waste via anaerobic digestion process: A review. Solid state science and technology, 25(1), 29-39.
Okorie, A. (2015). Housing infrastructural facilities as determinants of rental values of residential properties in Osogbo, Osun State Nigeria. Journal of Research in Business Economics and Management (JBREM), 1(1), 7-14.
Park, G. R., & Kim, J. (2023). Trajectories of life satisfaction before and after homeownership: The role of housing affordability stress. Journal of Happiness Studies, 24(1), 397-408. https://doi.org/10.1007/s10902-022-00601-7
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of political economy, 82(1), 34-55. https://doi.org/10.1086/260169
Shen, Y., & Karimi, K. (2015). Understanding the roles of urban configuration on spatial heterogeneity and submarket regionalisation of house price pattern in a mix-scale hedonic model: the case of Shanghai, China. In SSS 2015-10th International Space Syntax Symposium, Space Syntax Laboratory, The Bartlett School of Architecture, UCL.
Shrestha, N. (2020). Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39-42. https://doi.org/10.12691/ajams-8-2-1
Sipan, I., Mar Iman, A. H., & Razali, M. N. (2018). Spatial–temporal neighbourhood-level house price index. International Journal of Housing Markets and Analysis, 11(2), 386-411. https://doi.org/10.1108/IJHMA-03-2017-0027
Sitar, M., & Krajnc, K. (2008). Sustainable housing renewal. American Journal of Applied Sciences, 5(10), 61-66.
Sodiya, A. K., Oyedeji, O. J., & Bello, I. K. (2016). Examination of tenants perceptions of finishes and facilities in residential properties of public housing estates in Abeokuta Metropolis. Pearl Journal of Management, Social Science and Humanities, 2(2), 25-32.
Song, Z., Wilhelmsson, M., & Yang, Z. (2022). Constructing segmented rental housing indices: evidence from Beijing, China. Property Management, 40(3), 409-436. https://doi.org/10.1108/PM-07-2021-0052
Tavares, F. O., Pacheco, L. D., & Almeida, L. G. (2019). Preferences in university residences: A confirmatory study. African Journal of Hospitality, Tourism and Leisure, 8(2), 1-10.
Thomsen, J. (2007). Home experiences in student housing: about temporary homes and institutional character. Journal of Youth Studies. 10(5), 577-596. https://doi.org/10.1080/13676260701582062
Tobler, W. R. (1969). Geographical filters and their inverses. Geographical Analysis, 1(3), 234-253.
Unwin, D. J. (1996). GIS, spatial analysis and spatial statistics. Progress in Human Geography, 20(4), 540-551. https://doi.org/10.1177/030913259602000408
Yunus, S., Yusuf, Y. A, Ilah, S. K. & Yakubu, M. D. (2018). Spatial Analysis of Off-Campus Student Housing Around the New Campus of Bayero University, Kano, Nigeria. Savanna, 24(2), 358-371.
Verhetsel, A., Kessels, R., Zijlstra, T., & Van Bavel, M. (2017). Housing preferences among students: collective housing versus individual accommodations? A stated preference study in Antwerp (Belgium). Journal of Housing and the Built Environment, 32, 449-470. https://doi.org/10.1007/s10901-016-9522-5
Wilhelmsson, M., Ceccato, V., & Gerell, M. (2022). What effect does gun-related violence have on the attractiveness of a residential area? The case of Stockholm, Sweden. Journal of European Real Estate Research, 15(1), 39-57. https://doi.org/10.1108/JERER-03-2021-0015
Xiao, Y., Hui, E. C., & Wen, H. (2020). The housing market impacts of human activities in public spaces: The case of the square dancing. Urban forestry & urban greening, 54, 126769. https://doi.org/10.1016/j.ufug.2020.126769
Zhang, Z., Tan, S., & Tang, W. (2015). A GIS-based spatial analysis of housing price and road density in proximity to urban lakes in Wuhan City, China. Chinese Geographical Science, 25, 775-790. https://doi.org/10.1007/s11769-015-0788-4
Zubairu, G. A., Sulaiman, N., & Mohammed, M. I. (2018). Student Housing a Resilient Housing Rental Market: Case of Federal Polytechnic Neighbourhood Bauchi, Nigeria. Traektoriâ Nauki= Path of Science, 4(3), 4008-4017