Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32759
Urban Big Data: An Experimental Approach to Building-Value Estimation Using Web-Based Data

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

Abstract:

Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: Big data, building-value analysis, machine learning, price prediction.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130307

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1106

References:


[1] C. B. Akerson, The appraiser's workbook. Amer Inst of Real Estate appraisers, 1985
[2] Appraisal institute (U.S.), The Appraisal of Real Estate, Chicago, Ill. : Appraisal Institute, c1996.
[3] A. J. Gonzalez and R. Laureano-Ortiz, “A case-based reasoning approach to real estate property appraisal,” Expert Systems with Applications, vol. 4, no. 2, 1992, pp. 229-246.
[4] V. Kontrimas and A. Verikas, “The mass appraisal of the real estate by computational intelligence,” Applied Soft Computing, vol. 11, no. 1, 2011, pp. 443-448.
[5] K. L. Priddy, S. K. Rogers, D. W. Ruck, G. L. Tarr and M. Kabrisky, Bayesian Selection of Important Features for Feedforward Neural Networks, Neurocomputing 5, 1993, pp. 91–103.
[6] R. Klinkenberg (Ed.), RapidMiner: Data mining use cases and business analytics applications, Chapman and Hall/CRC, 2013.
[7] Rapid miner - rapidminer.com (2017. 3. 13)
[8] Naver real estate - land.naver.com (2017. 3. 13)
[9] Registered building data by the Seoul City - kras.seoul.go.kr/land_info (2017. 3. 13)