SpringerLink
Log in

The nexus between transportation infrastructure and housing prices in metropolitan regions

  • Article
  • Published:
Journal of Housing and the Built Environment Aims and scope Submit manuscript

Abstract

This study investigates factors influencing housing prices in Tehran's metropolitan area and predicts their values. We analysed a sample of 32,162 housing units using Hedonic Model and Artificial Neural Networks for prediction and comparison. The Results indicate that distance to certain transportation facilities, such as airports, bus terminals, and road intersections, positively affects housing prices, potentially due to their capacity to attract traffic and environmental externalities. Conversely, distance to facilities like public bike stations and bus rapid transit stations negatively impacts prices, signalling their role in enhancing accessibility. Notably, ANN outperformed hedonic in predicting housing prices. These findings hold significant implications for policymakers, investors, and housing market stakeholders, shedding light on the intricate relationship between transportation infrastructure and housing prices. The study underscores the importance of employing diverse modelling methods to capture the non-linear dynamics of housing markets. These insights are crucial for evidence-based decision-making and can inform more effective policies and strategies in metropolitan Tehran and other similar cities. By aligning transportation policies with housing market needs, cities can enhance urban development and promote sustainable growth.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The raw datasets are available from the first author on reasonable request.

References

  • Abdi Daneshpour, Z., & Shafiee, A. (2018). Analysing spatial inequality in Tehran’s Housing System, via changing prices during 1992–2016. Geography and Development Iranian Journal, 16(52), 267–292.

    Google Scholar 

  • Abdi, M. H., & Soltani, A. (2022). Which fabric/scale is better for transit-oriented urban design: Case studies in a develo** Country. Sustainability, 14(12), 7338.

    Article  Google Scholar 

  • Agnihotri, V., & Paul, S. K. (2023). Housing market shifts favouring transit-oriented development in emerging economies: the link between metro rails and housing price dynamics in Delhi. International Journal of Housing Markets and Analysis. pp. 1-24

  • Allan, A., Soltani, A., Abdi, M. H., & Zarei, M. (2022) Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review Land 11(8,) 1222 https://doi.org/10.3390/land11081222

  • Allen, H. (2013). An integrated approach to public transport, Tehran. Islamic Republic of Iran.

    Google Scholar 

  • AlQuhtani, S., & Anjomani, A. (2019). Do rail transit stations affect housing value changes? The Dallas Fort-Worth metropolitan area case and implications. Journal of Transport Geography, 79, 102463.

    Article  Google Scholar 

  • Aryan, V., Majedi, H., & Saeideh Zarabadi, Z. S. (2019). The impact of physical-spatial components on residential land value in Zaferanieh Neighborhood, Tehran. Journal of Architecture and Urban Planning, 11(22), 19–36.

    Google Scholar 

  • Astrachan, C. B., Patel, V. K., & Wanzenried, G. (2014). A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. Journal of Family Business Strategy, 5(1), 116–128.

    Article  Google Scholar 

  • Azmoodeh, M., Haghighi, F., & Motieyan, H. (2023). The capability approach and social equity in transport: Understanding factors affecting capabilities of urban residents using structural equation modeling Transport Policy 142137–151. https://doi.org/10.1016/j.tranpol.2023.08.010

    Article  Google Scholar 

  • Azmoodeh, M., Haghighi, F., Motieyan, H., Javad, M., & Tilaki, M. (2020). Investigating the relationship between housing policy and accessibility, based on develo** a multi-perspectives accessibility index: a case study in Tehran, Iran. Journal of Housing and the Built Environment, 35(4), 1237–1259. https://doi.org/10.1007/s10901-020-09738-4

  • Bangura, M., & Lee, C. L. (2023). The determinants of homeownership affordability in greater Sydney: Evidence from a submarket analysis. Housing Studies, 38(2), 206–232.

    Article  Google Scholar 

  • Batóg, J., Foryś, I., Gaca, R., Głuszak, M., & Konowalczuk, J. (2019). Investigating the impact of airport noise and land use restrictions on house prices: Evidence from selected regional airports in Poland. Sustainability, 11(2), 412.

    Article  Google Scholar 

  • Bełej, M. (2018). Synergistic network connectivity among urban areas based on non-linear model of housing prices dynamics. Real Estate Management and Valuation, 26(4), 22–34.

    Article  Google Scholar 

  • Bełej, M., Cellmer, R., & Głuszak, M. (2020). The impact of airport proximity on single-family house prices—Evidence from Poland. Sustainability, 12(19), 7928.

    Article  Google Scholar 

  • Bhat, M. R., Jiao, J., & Azimian, A. (2023). The impact of COVID-19 on home value in major Texas cities. International Journal of Housing Markets and Analysis, 16(3), 616–627.

    Article  Google Scholar 

  • Bohman, H., & Nilsson, D. (2021). Borrowed sizes. Journal of Transport and Land Use, 14(1), 87–103.

    Article  Google Scholar 

  • Cengiz, E. C., & Çelik, H. M. (2019). Investigation of the impact of railways on housing values; the case of Istanbul, Turkey. International Journal of Transport Development and Integration, 3(4), 295–305.

    Article  Google Scholar 

  • Central Bank of Iran. (2022). Tehran Statistical Yearbook. Available at: http://www.cbi.ir, visited 10 Jan 2023.

  • Cervero, R., & Kang, C. D. (2011). Bus rapid transit impacts on land uses and land values in Seoul, Korea. Transport Policy, 18(1), 102–116.

    Article  Google Scholar 

  • Chau, K. W., Wong, S. K., & Yiu, C. Y. (2005). Adjusting for non-linear age effects in the repeat sales index. The Journal of Real Estate Finance and Economics, 31, 137–153.

    Article  Google Scholar 

  • Chenary, K., Soltani, A., & Sharifi, A. (2023). Street network patterns for mitigating urban heat islands in arid climates. International Journal of Digital Earth, 16(1), 3145–3161. https://doi.org/10.1080/17538947.2023.2243901

  • Chen, H., Liu, H., Chu, X., Zhang, L., & Yan, B. (2020). A two-phased SEM-neural network approach for consumer preference analysis. Advanced Engineering Informatics, 46, 101156.

    Article  Google Scholar 

  • Chen, Y., Jiao, J., & Farahi, A. (2023). Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit, and density factors on single-family housing price. Cities, 140, 104432.

    Article  Google Scholar 

  • Chernobai, E., Reibel, M., & Carney, M. (2011). Nonlinear spatial and temporal effects of highway construction on house prices. The Journal of Real Estate Finance and Economics, 42, 348–370.

    Article  Google Scholar 

  • Chiarazzo, V., Coppola, P., Dell’Olio, L., Ibeas, A., & Ottomanelli, M. (2014a). The effects of environmental quality on residential choice location. Procedia-Social and Behavioral Sciences, 162, 178–187.

    Article  Google Scholar 

  • Chiarazzo, V., dell’Olio, L., Ibeas, Á., & Ottomanelli, M. (2014b). Modeling the effects of environmental impacts and accessibility on real estate prices in industrial cities. Procedia-Social and Behavioral Sciences, 111, 460–469.

    Article  Google Scholar 

  • Chica-Olmo, J., Cano-Guervos, R., & Chica-Rivas, M. (2019). Estimation of housing price variations using spatio-temporal data. Sustainability, 11(6), 1551.

    Article  Google Scholar 

  • Churchill, S. A., Baako, K. T., Mintah, K., & Zhang, Q. (2021). Transport infrastructure and house prices in the long run. Transport Policy, 112, 1–12.

    Article  Google Scholar 

  • Conrow, L., Mooney, S., & Wentz, E. A. (2021). The association between residential housing prices, bicycle infrastructure and ridership volumes. Urban Studies, 58(4), 787–808.

    Article  Google Scholar 

  • Debrezion, G., Pels, E., & Rietveld, P. (2011). The impact of rail transport on real estate prices: An empirical analysis of the Dutch housing market. Urban Studies, 48(5), 997–1015.

    Article  Google Scholar 

  • D’Elia, V. V., Grand, M. C., & León, S. (2020). Bus rapid transit and property values in Buenos Aires: Combined spatial hedonic pricing and propensity score techniques. Research in Transportation Economics, 80, 100814.

    Article  Google Scholar 

  • Ding, Y., Zhou, J., & Li, Y. (2015). Transit accessibility measures incorporating the temporal dimension. Cities, 46, 55–66.

    Article  Google Scholar 

  • Dubé, J., Thériault, M., & Des Rosiers, F. (2013). Commuter rail accessibility and house values: The case of the Montreal South Shore, Canada, 1992–2009. Transportation Research Part A: Policy and Practice, 54, 49–66.

    Google Scholar 

  • Farhadi, M. (2015). Transport infrastructure and long-run economic growth in OECD countries. Transportation Research Part A: Policy and Practice, 74, 73–90.

    Google Scholar 

  • Feng, S. T., Peng, C. W., Yang, C. H., & Chen, P. W. (2021). Non-linear relationships between house size and price. International Journal of Strategic Property Management, 25(3), 240–253.

    Article  Google Scholar 

  • Forouhar, A., & Hasankhani, M. (2018). The effect of Tehran metro rail system on residential property values: A comparative analysis between high-income and low-income neighbourhoods. Urban Studies, 55(16), 3503–3524.

    Article  Google Scholar 

  • Gasparėnienė, L., Remeikienė, R., & Skuka, A. (2016). Assessment of the impact of macroeconomic factors on housing price level: Lithuanian case. Intellectual Economics, 10(2), 122–127.

    Article  Google Scholar 

  • Geng, B., Bao, H., & Liang, Y. (2015). A study of the effect of a high-speed rail station on spatial variations in housing price based on the hedonic model. Habitat International, 49, 333–339.

    Article  Google Scholar 

  • Ghanbari, R., Heidarimozaffar, M., Soltani, A., & Arefi, H. (2023). Land surface temperature analysis in densely populated zones from the perspective of spectral indices and urban morphology. International Journal of Environmental Science and Technology, 20, 2883–2902.

    Article  Google Scholar 

  • Ghasemi, K., Hamzenejad, M., & Meshkini, A. (2018). The spatial analysis of the livability of 22 districts of Tehran Metropolis using multi-criteria decision making approaches. Sustainable Cities and Society, 38, 382–404.

    Article  Google Scholar 

  • Grznar, J., Prasad, S., & Tata, J. (2007). Neural networks and organizational systems: Modeling non-linear relationships. European Journal of Operational Research, 181(2), 939–955.

    Article  Google Scholar 

  • Guan, C., & Peiser, R. B. (2018). Accessibility, urban form, and property value. Journal of Transport and Land Use, 11(1), 1057–1080.

    Article  Google Scholar 

  • Guan, C., Tan, M. J., & Peiser, R. (2021). Spatiotemporal effects of proximity to metro extension on housing price dynamics in Manhattan, New York City. Journal of Transport and Land Use, 14(1), 1295–1315.

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Emerald Group Publishing Limited.

    Book  Google Scholar 

  • Hopkins, E. A. (2018). The influence of public transportation on housing values. International Journal of Sustainable Development & World Ecology, 25(3), 206–215.

    Article  Google Scholar 

  • Hu, S., Yang, S., Li, W., Zhang, C., & Xu, F. (2016). Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China. Applied Geography, 68, 48–56.

    Article  Google Scholar 

  • Huang, H., & Yin, L. (2015). Creating sustainable urban built environments: An application of hedonic house price models in Wuhan, China. Journal of Housing and the Built Environment, 30(2), 219–235.

    Article  Google Scholar 

  • Huang, N., Pang, J., & Yang, Y. (2023). JUE Insight: COVID-19 and household preference for urban density in China. Journal of Urban Economics, 133, 103487.

    Article  Google Scholar 

  • Kurvinen, A., & Sorri, J. (2016). Bus transportation accessibility-does it impact housing values? Working paper, Tampere University of Technology.

  • Lee, C. L., & Locke, M. (2021). The effectiveness of passive land value capture mechanisms in funding infrastructure. Journal of Property Investment & Finance, 39(3), 283–293.

    Article  Google Scholar 

  • Li, H., Wei, Y. D., Wu, Y., & Tian, G. (2019a). Analyzing housing prices in Shanghai with open data: Amenity, accessibility and urban structure. Cities, 91, 165–179.

    Article  Google Scholar 

  • Li, S., Chen, L., & Zhao, P. (2019b). The impact of metro services on housing prices: A case study from Bei**g. Transportation, 46(4), 1291–1317.

    Article  Google Scholar 

  • Liang, J., Koo, K. M., & Lee, C. L. (2021). Transportation infrastructure improvement and real estate value: Impact of level crossing removal project on housing prices. Transportation. https://doi.org/10.1007/s11116-020-10157-1

    Article  Google Scholar 

  • Limsombunchai, V., & Samarasinghe, S. (2004). House price prediction using artificial neural network: A comparative study with hedonic price model. Applied Economics Journal, 9, 2.

    Google Scholar 

  • Liu, F., Min, M., Zhao, K., & Hu, W. (2020). Spatial-temporal variation in the impacts of urban infrastructure on housing prices in Wuhan. China. Sustainability, 12(3), 1281.

    Article  Google Scholar 

  • Liu, Z., Wang, Y., & Tao, R. (2013). Social capital and migrant housing experiences in urban China: A structural equation modeling analysis. Housing Studies, 28(8), 1155–1174.

    Article  Google Scholar 

  • Martínez, L. M., & Viegas, J. M. (2009). Effects of transportation accessibility on residential property values: Hedonic price model in the Lisbon, Portugal, metropolitan area. Transportation Research Record, 2115(1), 127–137.

    Article  Google Scholar 

  • Mathur, S. (2022). Non-linear and weakly monotonic relationship between school quality and house prices. Land Use Policy, 113, 105922.

    Article  Google Scholar 

  • McIntosh, J., Newman, P., Trubka, R., & Kenworthy, J. (2017). Framework for land value capture from investments in transit in car-dependent cities. Journal of Transport and Land Use, 10(1), 155–185.

    Google Scholar 

  • Mohammad, S. I., Graham, D. J., Melo, P. C., & Anderson, R. J. (2013). A meta-analysis of the impact of rail projects on land and property values. Transportation Research Part A: Policy and Practice, 50, 158–170.

    Google Scholar 

  • Mulley, C., & Tsai, C.-H.P. (2016). When and how much does new transport infrastructure add to property values? Evidence from the bus rapid transit system in Sydney, Australia. Transport Policy, 51, 15–23.

    Article  Google Scholar 

  • Ngo, T., Squires, G., McCord, M., & Lo, D. (2023). House prices, airport location proximity, air traffic volume and the COVID-19 effect. Regional Studies, Regional Science, 10(1), 418–438.

    Article  Google Scholar 

  • Padeiro, M. (2013). Transport infrastructures and employment growth in the Paris metropolitan margins. Journal of Transport Geography, 31, 44–53.

    Article  Google Scholar 

  • Paliska, D., & Drobne, S. (2020). Impact of new motorway on housing prices in rural North-East Slovenia. Journal of Transport Geography, 88, 102831.

    Article  Google Scholar 

  • Pazhuhan, M., Soltani, A., Ghadami, M., Shahraki, S. Z., & Salvati, L. (2022). Environmentally friendly behaviors and commuting patterns among tertiary students: the case of University of Tehran, Iran. Environment, Development and Sustainability, 24(5), 7435–7454. https://doi.org/10.1007/s10668-022-02266-x

  • Peterson, S., & Flanagan, A. (2009). Neural network hedonic pricing models in mass real estate appraisal. Journal of Real Estate Research, 31(2), 147–164.

    Article  Google Scholar 

  • Qadikolaei, M. R., Zali, N., & Soltani, A. (2022). Spatiotemporal investigation of the digital divide, the case study of Iranian Provinces. Environment, Development and Sustainability, 23(8), 32–39.

    Google Scholar 

  • Ren, H., Yuan, N., & Hu, H. (2019). Housing quality and its determinants in rural China: A structural equation model analysis. Journal of Housing and the Built Environment, 34, 313–329.

    Article  Google Scholar 

  • Sadayuki, T. (2018). Measuring the spatial effect of multiple sites: An application to housing rent and public transportation in Tokyo, Japan. Regional Science and Urban Economics, 70, 155–173.

    Article  Google Scholar 

  • Sainaghi, R., & Chica-Olmo, J. (2022). The effects of location before and during COVID-19: Impacts on revenue of Airbnb listings in Milan (Italy). Annals of Tourism Research, 96, 103464.

    Article  Google Scholar 

  • Selim, H. (2009). Determinants of house prices in Turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications, 36(2), 2843–2852.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Seo, W., & Nam, H. K. (2019). Trade-off relationship between public transportation accessibility and household economy: Analysis of subway access values by housing size. Cities, 87, 247–258.

    Article  Google Scholar 

  • Soltani, A., Heydari, M., Aghaei, F., & Pettit, C. J. (2022). Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms. Cities, 131, 103941.

    Article  Google Scholar 

  • Soltani, A., Hosseinpour, M., & Hajizadeh, A., (2017) Urban Sprawl in Iranian Medium-sized Cities; Investigating the Role of Masterplans. Journal of Sustainable Development, 10(1), 122 https://doi.org/10.5539/jsd.v10n1p122

  • Soltani, A., Pettit, C. J., Heydari, M., & Aghaei, F. (2021). Housing price variations using spatio-temporal data mining techniques. Journal of Housing and the Built Environment. https://doi.org/10.1007/s10901-020-09811-y

    Article  Google Scholar 

  • Soltani, A., Zali, N., Mirzaei, R., Heydari, M., Rahimi, A., & Hamidi, A. (2023). Multilevel Impacts of Urban Amenities on Housing Price in Tehran Iran. Journal of Urban Planning and Development, 149(4,) https://doi.org/10.1061/JUPDDM.UPENG-4434

  • Song, Z., Cao, M., Han, T., & Hickman, R. (2019). Public transport accessibility and housing value uplift: Evidence from the Docklands light railway in London. Case Studies on Transport Policy, 7(3), 607–616.

    Article  Google Scholar 

  • Soundararaj, B., Pettit, C., & Lock, O. (2022). Using real-time dashboards to monitor the impact of disruptive events on real estate market. Case of COVID-19 pandemic in Australia. Computational Urban Science, 2, 14.

    Article  Google Scholar 

  • Soundararaj, B., & Pettit, C. (2021). Using eye-tracking to support big data driven property valuation tools. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 46, 111–114.

    Article  Google Scholar 

  • Su, S., Zhang, J., He, S., Zhang, H., Hu, L., & Kang, M. (2021). Unraveling the impact of TOD on housing rental prices and implications on spatial planning: A comparative analysis of five Chinese megacities. Habitat International, 107, 102309.

    Article  Google Scholar 

  • Theisen, T., & Emblem, A. W. (2021). The road to higher prices: Will improved road standards lead to higher housing prices? The Journal of Real Estate Finance and Economics, 62(2), 258–282.

    Article  Google Scholar 

  • Tsui, W. H. K., & Fung, M. K. Y. (2016). Causality between business travel and trade volumes: Empirical evidence from Hong Kong. Tourism Management, 52, 395–404.

    Article  Google Scholar 

  • Tuofu, H., Qingyun, H., Dongxiao, Y., & **ao, O. (2021). Evaluating the impact of urban blue space accessibility on housing price: A spatial quantile regression approach applied in Changsha. China. Frontiers in Environmental Science, 9, 164.

    Google Scholar 

  • Wang, J., & Lee, C. L. (2022). The value of air quality in housing markets: A comparative study of housing sale and rental markets in China. Energy Policy, 160, 112601.

    Article  Google Scholar 

  • Wardman, M. (2001). A review of British evidence on time and service quality valuations. Transportation Research Part e: Logistics and Transportation Review, 37(2–3), 107–128.

    Article  Google Scholar 

  • Welch, T. F., Gehrke, S. R., & Wang, F. (2016). Long-term impact of network access to bike facilities and public transit stations on housing sales prices in Portland, Oregon. Journal of Transport Geography, 54, 264–272.

    Article  Google Scholar 

  • Yang, L., Chau, K., Szeto, W., Cui, X., & Wang, X. (2020). Accessibility to transit, by transit, and property prices: Spatially varying relationships. Transportation Research Part D: Transport and Environment, 85, 102387.

    Article  Google Scholar 

  • Yang, L., Zhou, J., & Shyr, O. F. (2019). Does bus accessibility affect property prices? Cities, 84, 56–65.

    Article  Google Scholar 

  • Yang, Z., Li, X., Guo, Y., & Qian, X. (2023). Understanding active transportation accessibility’s impacts on polycentric and monocentric cities’ housing price. Research in Transportation Economics, 98, 101282.

    Article  Google Scholar 

  • Yang, Z., Yi, C., Zhang, W., & Zhang, C. (2014). Affordability of housing and accessibility of public services: Evaluation of housing programs in Bei**g. Journal of Housing and the Built Environment, 29(3), 521–540.

    Article  Google Scholar 

  • Yazdanifard, Y., Talebian, M., & Joshaghani, H. (2021). Metro station inauguration, housing prices, and transportation accessibility. Journal of Transport and Land Use, 14(1), 537–561.

    Article  Google Scholar 

  • Yuan, F., Wei, Y. D., & Wu, J. (2020). Amenity effects of urban facilities on housing prices in China: Accessibility, scarcity, and urban spaces. Cities, 96, 102433.

    Article  Google Scholar 

  • Zhan, C., Wu, Z., Liu, Y., **e, Z., & Chen, W. (2020). Housing prices prediction with deep learning: an application for the real estate market in Taiwan. In 2020 IEEE 18th International Conference on Industrial Informatics (INDIN) (Vol. 1, pp. 719–724). IEEE.

  • Zhang, L., Zhou, T., & Mao, C. (2019). Does the difference in urban public facility allocation cause spatial inequality in housing prices? Evidence from Chongqing, China. Sustainability, 11(21), 6096.

    Article  Google Scholar 

  • Zhang, Y., Rahman, A., & Miller, E. (2023). Longitudinal modelling of housing prices with machine learning and temporal regression. International Journal of Housing Markets and Analysis, 16(4), 693–715.

    Article  Google Scholar 

  • Zheng, X., Peng, W., & Hu, M. (2020). Airport noise and house prices: A quasi-experimental design study. Land Use Policy, 90, 104287.

    Article  Google Scholar 

  • Zhou, J., Yang, L., & Li, L. (2018). The implications of high-speed rail for Chinese cities: Connectivity and accessibility. Transportation Research Part A: Policy and Practice, 116, 308–326.

    Google Scholar 

Download references

Funding

No funding was available for this research.

Author information

Authors and Affiliations

  1. FHMRI, Flinders University, Bedford Park, 5042, Australia, SA

    Ali Soltani

  2. Department of Urban Planning, Shiraz University, Shiraz, Iran

    Ali Soltani

  3. Department of Urban Planning, University of Guilan, Rasht, Iran

    Nader Zali, Hadi Aghajani, Farshid Hashemzadeh & Ali Rahimi

  4. Department of Urban Planning, Modares University, Tehran, Iran

    Mohammad Heydari

Authors
  1. Ali Soltani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nader Zali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hadi Aghajani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Farshid Hashemzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ali Rahimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohammad Heydari
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AS: Project management; Data analysis; Writing; Editing; NZ: Editing; Resources; HA: Literature review; Data analysis; Modelling; Writing; EndNote; Software; FHG: Literature review; Data analysis; Writing; EndNote; Software; AR: Literature review; Writing; MH: Collecting data; Modelling; Software. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Ali Soltani.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table

Table 5 Primary dependent and independent variables
Full size table

5

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Soltani, A., Zali, N., Aghajani, H. et al. The nexus between transportation infrastructure and housing prices in metropolitan regions. J Hous and the Built Environ 39, 787–812 (2024). https://doi.org/10.1007/s10901-023-10085-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10901-023-10085-3

Keywords

Search

Navigation