تحلیل وابستگی فضایی قیمت مسکن بین نواحی 22 گانه شهر تهران

نوع مقاله : مقاله پژوهشی

نویسندگان

• بهرام حکمت ¹
• شکوفه فرهمند ²
• نعمت الله اکبری ³

¹ دانشجوی دکتری اقتصاد دانشگاه اصفهان

² دانشیار گروه اقتصاد دانشگاه اصفهان

³ استاد گروه اقتصاد دانشگاه اصفهان

https://doi.org/10.22067/erd.2022.72814.1073

چکیده

چکیده
به دلیل اهمیت نقش مسکن در اقتصاد، بخصوص در کلان‌شهری مانند تهران، تحلیل قیمت مسکن و شناخت عوامل تأثیرپذیر بر روی قیمت مسکن از اهمیت خاصی برخوردار است. مطالعات مختلف نشان می‌دهند که تغییرات قیمت مسکن در یک ناحیه از نواحی مجاور خود تأثیرپذیر است؛ بنابراین تحلیل قیمت مسکن بدون در نظر گرفتن تفکر فضایی عاری از خطا نخواهد بود. در این مقاله با استفاده از اقتصادسنجی فضایی، به تحلیل قیمت مسکن بین نواحی 22 گانه شهر تهران پرداخته شد. در این راستا، متغیرهای تعیین‌کننده نرخ رشد قیمت مسکن در نواحی 22 گانه شهر تهران به کمک مدل خود رگرسیون فضایی اثر ثابت پویا  مشخص شدند. نتایج حاکی از یک نوع وابستگی فضایی نرخ رشد قیمت مسکن بین نواحی شهر تهران بوده است. متغیرهای نرخ رشد جمعیت و نرخ رشد درآمد سرانه اثر معنا‌دار مثبتی بر روی نرخ رشد قیمت مسکن دارند. متغیر تعداد پروانه‌های ساختمانی اثر منفی بر روی قیمت مسکن داشته است. رابطه معناداری بین قیمت مسکن و نرخ بیکاری یافت نشد. نتایج وجود یک همبستگی فضایی نرخ رشد قیمت مسکن در بین نواحی 22 گانه را تأیید می‌کند. در واقع تغییرات نرخ رشد قیمت مسکن در یک ناحیه از نرخ رشد قیمت مسکن نواحی هم‌جوار خود اثر مثبتی می‌پذیرد. بر اساس نمودار موران محلی مشخص شد، همبستگی فضایی نرخ رشد قیمت مسکن در نواحی جنوب شهر با نواحی شمال شهر تهران متفاوت است.

کلیدواژه‌ها

• "قیمت مسکن
• وابستگی فضایی
• پانل اثر‌ثابت
• مدل فضایی پویا

عنوان مقاله [English]

Spatial analysis of housing prices in 22 urban districts of Tehran

نویسندگان [English]

• bahram hekmat ¹
• Shekoofeh Farahmand ²
• nematallah akbari ³

¹ PhD candidate of Economics, University of Isfahan

² Associate professor, Department of Economics, University of Isfahan

³ Full professor, Department of Economics, University of Isfahan

چکیده [English]

 
1- INTRODUCTION
Consideration of the importance of the role of housing in the economy, especially in metropolitan areas such as Tehran, the analysis of house pricing and identifying the factors affecting housing prices is very importance. A noteworthy point in the study of housing price changes in the metropolis of Tehran is that the rate of price growth in different areas of Tehran has not been the same. Experience shows that price changes start in one district and then spread to other districts. Therefore, we should consider proximity and spatial dependence of housing prices in urban distrists.
 
2- THEORETICAL FRAMEWORK
In the analysis of housing price data, the spatial dependence between observations must be taken into consideration. Accordingly, many experimental studies have shown strong evidence of spatial dependence in the housing market between urban areas. Spatial dependence on housing prices is also referred to as the wave effect, implying that housing prices in a district cause changes in neighboring districts' prices. Behavioral economics is a theoretical basis that can express the phenomenon of spatial dependence between urban districts. Regarding the theories of behavioral economics, nearby urban areas have the same culture, history, environment, and policies. Other phenomena that express the spatial dependence in the housing market of urban districts include migration theory, capital transfer, arbitrage, and spatial patterns.
 
3- METHODOLOGY
 A critical issue in studies using spatial econometric techniques is the choice of the type of spatial model. Depending on the type of spatial interaction, we will encounter a variety of spatial models. To select spatial models, we can first consider the general spatial model and with the relevant tests to ensure the existence of the type of spatial interaction factor. Then, the appropriate type of model can be selected. According to Elhorst (2014), using the spatial lag fixed-effect model, we have modeled the housing prices in 22 districts of Tehran city in this study. Some variables affecting housing prices are related to the demand side of the housing market, and some are related to the supply side. Here, the explanatory variables in the model are the annual population growth rate of the districts, and the annual growth rate of the real disposable per capita income, which shows the demand side influencing the housing price, as well as the number of building permits and the annual unemployment rate as the supply-side variables that affect housing prices. The dependent variable is the districts' average annual real growth rate of housing prices.
 
4- RESULTS & DISCUSSION
According to the spatial specification results, the dynamic SAR model has been selected as the appropriate model. The estimated coefficient of the spatial lag of the dependent variable with a time lag is 0.033, which is statistically significant and indicates a positive effect. It implies that the spatial lag of the dependent variable with a time lag has influenced the growth rate of housing prices in the 22 districts of Tehran. Among the explanatory variables affecting the growth rate of housing prices in the 22 districts of Tehran, it is observed that, except for the unemployment rate variable, the other variables have a significant effect on the growth rate of housing prices in Tehran districts. The analysis of the local Moran scatterplot demonstrates that the spatial correlation of the housing price growth rate in the northern districts of Tehran is different from the southern districts.
 
5- CONCLUSIONS & SUGGESTIONS
Among the explanatory variables affecting the growth rate of housing prices in Tehran districts, except for the unemployment rate, other variables have statistically significant impacts. Two variables of the growth rate of income per capita and population growth rate have positive effects. However, the number of building permits has negatively influenced housing prices' growth rate. The statistically significant estimated coefficient of spatial lag of the dependent variable in the model implies the spatial effects of the housing price growth rate. All the necessary tests indicate that the null hypothesis, which indicates the lack of spatial autocorrelation, has been rejected, and a spatial correlation among the housing price growth rates of districts has been confirmed. The local Moran scatterplot illustrates that the Spatial correlation of housing price growth rate in the northern districts of Tehran is entirely different from the southern areas of Tehran. It is recommended that urban policymakers should not ignore the spatial relationship between housing prices in 22 districts of Tehran. Also, it is recommended to the policymakers, due to the different spatial correlation rate of the housing price growth rate in the southern areas of Tehran city compared to the northern districts of the city, for each of the northern and southern areas of Tehran, they should make different policies.

کلیدواژه‌ها [English]

• Housing prices
• spatial dependence
• fixed effect model
• dynamic spatial panel data model

مراجع

 

References

 ‏Anselin, L. (1988). Spatial econometrics: methods and models (Vol. 4). Springer Science & Business Media.‏

Anselin, L. (2003). Spatial externalities, spatial multipliers, and spatial econometrics. International regional science review, 26(2), 153-166.

Aquaro, M., Bailey, N., & Pesaran, M. H. (2021). Estimation and inference for spatial models   with heterogeneous coefficients: an application to US house prices. Journal of Applied Econometrics, 36(1), 18-44.‏

Astuti, A. M., Zain, I., & Purnomo, J. D. T. (2020, March). A Review of Panel Data on Spatial Econometrics Models. In Journal of Physics: Conference Series (Vol. 1490, No. 1, p. 012032). IOP Publishing.

Brady, R. R. (2014). The spatial diffusion of regional housing prices across US states. Regional Science and Urban Economics, 46, 150-166.

Cellmer, R., Cichulska, A., & Bełej, M. (2020). Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression. International Journal of Geo – Information, 9(6), 380.‏

Cohen, J. P., Ioannides, Y. M., & Thanapisitikul, W. W. (2016). Spatial effects and house price dynamics in the USA. Journal of Housing Economics, 31, 1-13.‏

DeFusco, A., Ding, W., Ferreira, F., & Gyourko, J. (2018). The role of price spillovers in the American housing boom. Journal of Urban Economics, 108, 72-84.‏

Elhorst, J. P. (2014). Dynamic spatial panels: models, methods and inferences. In Spatial econometrics (pp. 95-119). Springer, Berlin, Heidelberg.‏

Elhorst, J. P. (2014). Spatial econometrics from cross-sectional data to spatial panels. Springer.‏

Elhorst, J. P. (2017). Spatial Panel Data Analysis. Encyclopedia of GIS, 2, 2050-2058.‏

Genesove, D., & Mayer, C. (2001). Loss aversion and seller behavior: Evidence from the housing market. The quarterly journal of economics, 116(4), 1233-1260.

Gong, Y., Boelhouwer, P., & de Haan, J. (2014). Spatial Dependence in House Prices: Evidence from China's Interurban Housing Market.‏

Guo, J., & Qu, X. (2019). Spatial interactive effects on housing prices in Shanghai and Beijing. Regional Science and Urban Economics, 76, 147-160.‏

Holly, S., Pesaran, M. H., & Yamagata, T. (2011). The spatial and temporal diffusion of house prices in the UK. Journal of urban economics, 69(1): 2-23.

Hyun, D., & Milcheva, S. (2018). Spatial dependence in apartment transaction prices during boom and bust. Regional Science and Urban Economics, 68, 36-45.‏,

Khalili Iraqi, Mansour and Mehrara, Mohsen. (2013). The Effect of Spatial Diffusion of Housing Price Changes in Iran Using Spatial Interruption Model and Combined Data. Quarterly Journal of Economic Research and Policy. No. 67, Fall 2013, Pages 48 - 25. (In persian)

Lassage, James and Pace, Kelly. (2009). Introduction to Space Sanpi Economics, translated by Jalaei Esfandiari. Abdolmajid and Jamshid Nejad, Arash, Noor Alam Publications.First Edition, 2013. (In persian)

Meen, G. (1999). Regional house prices and the ripple effect: a new interpretation. Housing studies, 14(6), 733-753.‏

Moralı, O., & Yılmaz, N. (2020). An analysis of spatial dependence in real estate prices. The Journal of Real Estate Finance and Economics, 1-23.‏

Oikarinen, E., Bourassa, S. C., Hoesli, M., & Engblom, J. (2018). US metropolitan house price dynamics. Journal of Urban Economics, 105, 54-69.‏

Pijnenburg, K. (2017). The spatial dimension of US house prices. Urban Studies, 54(2), 466-481.‏

Poor, Mohammadi, Mohammad, Reza et al. (2018). A comparative study of geographical weight regression approaches and ordinary squares in estimating place models. Journal of Geographical Research and Mapping. 23 (63), 76-53. (In persian)

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)

Taleblou, R., Mohamadi, T., & Pirdayeh, H. (2017). Analysis of Spatial Diffusion of Housing Price Changes in Iranian Provinces; Spatial Econometrics Approach. (In persian)‏

Wood, R. (2003). The information content of regional house prices: can they be used to improve national house price forecasts. Bank of England. Quarterly Bulletin, 43(3), 304.‏

Zhang, L., Wang, H., Song, Y., & Wen, H. (2019). Spatial spillover of house prices: An empirical study of the Yangtze Delta urban agglomeration in China. Sustainability, 11(2), 544.