How the Pattern Recognition Ability of Deep Learning Enhances Housing Price Estimation
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Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its ‘Deep Learning’ faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.
Abstract
Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its ‘Deep Learning’ faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.
참고문헌 (46)
- Abidoye, R. B., "Improving property valuation accuracy : A comparison of hedonic pricing model and artificial neural network", Pacific Rim Property Research Journal, 24(1), pp.71~83 (2018)
- Anselin, L., "GIS research infrastructure for spatial analysis of real estate markets", Journal of Housing Research, 9(1), pp.113~133 (1998)
- Basu, S., "Analysis of spatial autocorrelation in house prices", The Journal of Real Estate Finance and Economics, 17(1), pp.61~85 (1998)
- Can, A., "Specification and estimation of hedonic housing price models", Regional science and urban economics, 22(3), pp.453~474 (1992)
- Case, B., "Modeling spatial and temporal house price patterns : A comparison of four models", The Journal of Real Estate Finance and Economics, 29(2), pp.167~191 (2004)
- Chollet, F., "Deep learning with Python" (2017), Manning Publications Company
- 전해정, "유동성 관련 변수가 주택가격에 미치는 영향 및 정책적 시사점에 관한 연구", 한국경제지리학회지, 15(4), pp.585~600 (2012), 한국경제지리학회[DOI]
- Do, Q., "A neural network analysis of the effect of age on housing values", Journal of Real Estate Research, 8(2), pp.253~264 (1993)
- Dodgson, J. S., "Valuing residential properties with the hedonic method : A comparison with the results of professional valuations", Housing Studies, 5(3), pp.209~213 (1990)
- Dubin, R. A., "Spatial autocorrelation and neighborhood quality", Regional science and urban economics, 22(3), pp.433~452 (1992)
- Edmonds Jr, R. G., "A theoretical basis for hedonic regression : A research primer", Real Estate Economics, 12(1), pp.72~85 (1984)
- Fotheringham, A. S., "Geographically weighted regression : a natural evolution of the expansion method for spatial data analysis", Environment and planning A, 30(11), pp.1905~1927 (1998)
- García, N., "ANN+GIS:An automated system for property valuation", Neurocomputing, 71(4-6), pp.733~742 (2008)
- Géron, A., "Hands-On Machine Learning with Scikit-Learn and TensorFlow" (2017), O’Reilly Media
- Ghorbani, S., "Forecasting the house price for ahvaz city : the comparison of the hedonic and artificial neural network models", Journal of Urban Economics and Management, 5(19), pp.29~44 (2017)
- Goodman, A. C., "Hedonic prices, price indices and housing markets", Journal of urban economics, 5(4), pp.471~484 (1978)
- Goodman, A. C., "Housing market segmentation", Journal of housing economics, 7(2), pp.121~143 (1998)
- Haykin, S., "Neural Networks: A Comprehensive Foundation" (2003), Prentice Hall PTR
- Herath, S. K., "Hedonic house prices in the presence of spatial and temporal dynamics", Territorio Italia land Administration, Cadastre, Real Estate, 1(1), pp.39~49 (2011)
- Huang, B., "Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices", International Journal of Geographical Information Science, 24(3), pp.383~401 (2010)
- 정준호, "주택시장의 네트워크 구조 분석: 수도권 아파트 매매시장의 사례", 한국경제지리학회지, 17(2), pp.280~295 (2014), 한국경제지리학회[DOI]
- Khalafallah, A., "Neural network based model for predicting housing market performance", Tsinghua Science and Technology, 13(S1), pp.325~328 (2008)
- 김상진, "임대차 시장의 월세화와 주요 특성에 관한 연구: 서울시 강남 3구의 아파트 시장 사례", 한국경제지리학회지, 18(3), pp.348~365 (2015), 한국경제지리학회[DOI]
- Koki, S., "Deep Learning from Scratch" (2016), OREILLY JAPAN
- Law, S., "Take a look around : using street view and satellite images to estimate house prices", ACM Transactions on Intelligent Systems and Technology, 10(5), pp.1~19 (2019)
- Law, S., "Street-Frontage-Net : urban image classification using deep convolutional neural networks", International Journal of Geographical Information Science, 34(4), pp.681~707 (2020)
- LeCun, Y., "Deep learning", nature, 521(7553), pp.436~444 (2015)
- 이창로, "The Deep Learning Approach to Property Valuation: An Application of a Multilayer Neural Net Model for Estimating House", 한국지역개발학회지, 30(4), pp.179~202 (2018), 한국지역개발학회
- 이원도, "대기오염지수와 도시공간구조 특성에 관한 연구 : 서울시 토지이용과 교통자료를 바탕으로", 한국경제지리학회지, 14(2), pp.143~156 (2011), 한국경제지리학회[DOI]
- Lenk, M. M., "Hightech valuation: should artificial neural networks bypass the human valuer?", Journal of Property Valuation and Investment, 15(1), pp.8~26 (1997)
- Loo, W. K., "Predictability of HK-REITs returns using artificial neural network", Journal of Property Investment & Finance, 38(4), pp.291~307 (2019)
- Lu, B., "Geographically weighted regression with a non-Euclidean distance metric : a case study using hedonic house price data", International Journal of Geographical Information Science, 28(4), pp.660~681 (2014)
- Mimis, A., "Property valuation with artificial neural network : the case of Athens", Journal of Property Research, 30(2), pp.128~143 (2013)
- Osland, L., "An application of spatial econometrics in relation to hedonic house price modeling", Journal of Real Estate Research, 32(3), pp.289~320 (2010)
- Pai, P. F., "Using machine learning models and actual transaction data for predicting real estate prices", Applied Sciences, 10(17), pp.5832~ (2020)
- Peterson, S., "Neural network hedonic pricing models in mass real estate appraisal", Journal of Real Estate Research, 31(2), pp.147~164 (2009)
- Poursaeed, O., "Vision-based real estate price estimation", Machine Vision and Applications, 29(4), pp.667~676 (2018)
- Raschka, S., "Python Machine Learning:Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2" (2019), Packt Publishing Ltd
- Rosen, S., "Hedonic prices and implicit markets : product differentiation in pure competition", Journal of political economy, 82(1), pp.34~55 (1974)
- Stamou, M., "House price determinants in Athens : a spatial econometric approach", Journal of Property Research, 34(4), pp.269~284 (2017)
- Tay, D. P., "Artificial intelligence and the mass appraisal of residential apartments", Journal of Property Valuation and Investment, 10(2), pp.525~540 (1992)
- Tobler, W. R., "A computer movie simulating urban growth in the Detroit region", Economic geography, 46, pp.234~240 (1970)
- Worzala, E., "An exploration of neural networks and its application to real estate valuation", Journal of Real Estate Research, 10(2), pp.185~201 (1995)
- 양준석, "공공임대주택이 주변 전세시장에 미치는 효과: 서울시 장기전세주택(SHIFT)의 경우", 한국경제지리학회지, 20(3), pp.403~416 (2017), 한국경제지리학회[DOI]
- Zhou, X., "Modeling Housing Rent in the Atlanta Metropolitan Area Using Textual Information and Deep Learning", ISPRS International Journal of Geo-Information, 8(8), pp.349~ (2019)
- Zietz, J., "Determinants of house prices : a quantile regression approach", The Journal of Real Estate Finance and Economics, 37(4), pp.317~333 (2008)