Examination of the Factors Affecting Household Rental Housing Demand Through Data Mining

Year 2018, Volume: 13 Issue: 2, 227 - 238, 15.08.2018

Kübra Önder , Hüseyin Turgut

https://doi.org/10.17153/oguiibf.417717

Cited By: 3

Abstract

Houses are an irreplaceable
tool for the need for shelter However, as a result of the global economic,
cultural and technological changes encountered in recent years they have become
a source of assurance for the future and one of the most significant indicators
of life-style and wealth and social prestige as well as meeting the basic need
for shelter.  This causes house to become
a heterogeneous good and to involve many characteristics.  The effectiveness level of different characteristics
of the house on the market value of the house is predicted using hedonic
pricing model based on micro-economic theory. The aim of the study is to
analyze the factors affecting rental housing demand of households through data
mining methods. 2341 household data has been referenced and 49 data title was
selected as the basis ofthe study in which Household Budget Survey data of 2015
has been used. As a result of the study, it has been determined that Decision
Tree algorithm which is one of the Data Mining methods yielded the best result.

Keywords

Housing Market, Hedonic Pricing, Data Mining

Hanehalkının Konut Kira Talebine Etki Eden Faktörlerin Veri Madenciliği ile İncelenmesi

Abstract

Konut, barınma açısından
vazgeçilmez bir araç olmasına rağmen son yıllarda dünyada yaşanan ekonomik,
kültürel ve teknolojik değişimlerin bir sonucu olarak konut, barınma temel
ihtiyacını karşılama amacı dışında, gelecek için güvence kaynağı, yaşam tarzı
ve zenginliğin en önemli göstergelerinden biri olmakla kalmamış toplum içindeki
saygınlığın da ifadesi olmuştur. Bu durum konutun heterojen bir mal olmasına ve
birçok özelliği içinde barındırmasına neden olmaktadır. Konutun sahip olduğu
farklı özellikleri konutun piyasa değeri üzerinde ne derece etkin olduğu mikro
iktisat teorisine dayalı hedonik fiyat modeli ile tahmin edilmektedir. Çalışmanın
amacı, hanehalkının kiralık konut talebini etkileyen faktörleri Veri
Madenciliği süreç ile analiz etmektir. 2015 yılı Hanehalkı Bütçe Anket
verilerinin kullanıldığı çalışmada, 2341 hane verisi çalışmaya kaynaklık etmiş
ve 49 farklı veri başlığı temel alınmıştır. Çalışma sonucunda, Veri Madenciliği
yöntemlerinden Karar Ağacı Algoritmasının en iyi sonuç verdiği tespit
edilmiştir.

Keywords

Konut Piyasası, Hedonik Fiyatlama, Veri Madenciliği, Karar Ağaçları Yöntemi.

References

• Brian, T. Ratchford (1975), “The New Economic Theory of Consumer Behavior: An Interpretive Essay”, Journal of Con-sumer Research, Vol. 2, No. 2: 65-75, https://doi.org/10.1086/208617
• Chien, Chen-Fu; Chen, Li-Fei (2008), “Data Mining to Improve Personnel Selection and Enhance Human Capital: A Case Study in High-TechnologyIndustry”, Expert Systems with Applications, Vol. 34: No. 1: 280-290.
• Colin, Sheare (2000), “The CRISP-DM Model: The New Blueprint for Data Mining”, 13.
• Del Cacho, Carlos (2010), “A Comparison of Data Mining Methods for Mass Real Estate Appraisal”, MPRA, https://mpra.ub.uni-muenchen.de/27378/1/A_comparison_of_data_mining_methods_for_mass_real_estate_appraisal.pdf, (Erişim: 02.11.2017).
• DuMouchel, William (1999), “Bayesian Data Mining in Large Frequency Tables, with an Application the FDA Spon-taneous Reporting System”, The American Statistician, Vol. 53, No. 3: 177-190.
• Fan, Gang-Zhi; Ong, Seow Eng; Koh, Hian Chye (2006), “Determinants of House Price: A Decision Tree Approach”, Urban Studies, Vol. 43, No. 12: 2301-2315.
• Gan, Victor; Agarwal, Vaishali; Kim, Ben (2015), “Data Mining Analysis and Predictions of Real Estate Prices”, Issues in Information Systems, Vol. 16, Issue IV: 30-36
• Goodman, Allen C. (1978), “Hedonic Prices, Price Indices and Housing Markets”, Journal of Urban Economics, Vol. 5, No. 4: 471-484.
• Grannam, Farha (1997), Re-imagining the Global: Relocation and Local Identities in Cairo, içinde A.Öncü ve P. Weyland (der.), Space, Culture and Power: New Identities in Globalizing Cities (ss.119-139). Londra ve New Jersey: Zed.
• Haas, G. C. (1922), A statistical Analysis of Farm Sales in Blue Earth County, Minnesota, As a Basis For Farm Land Apprai-sal, Yayımlanmamış Yüksek Lisans Tezi, Minnesota Üniversitesi, ABD.
• Harrison, David; L. Rubinfeld, Daniel (1978), “Hedonic Housing Prices and the Demand for Clean Air”, Journal of Envi-ronmental Economics and Management, Vol. 5, No. 1: 81-102.
• Hidano, Noboru (2002), The Economic Valuation of Environment and Public Policy: A Hedonic Approach, Edward Elgar, Massachosetts.
• Jaen, Ruben D. (2002), “Data Mining: An Empirical Application in Real Estate Valuation”, In FLAIRS Conference http://www.aaai.org/Papers/FLAIRS/2002/FLAIRS02-062.pdf , (Erişim: 28.01.2018).
• Koev, Eugen (2003), Combining Classification and Hedonic Quality Adjustment in Constructing a House Price Index, Licentiate Thesis Department of Economics University of Helsinki, Finland.
• Lancaster, Kelvin J. (1966), “A New Approach to Consumer Theory”, Journal of Political Economy, Vol. 74, No. 2: 132–157.
• Lausch, Angela; Schmidt, Andreas; Tischendorf, Lutz (2015), “Data Mining and Linked Open Data–New Perspectives For Data Analysis in Environmental Research”, Ecological Modelling, Vol. 295: 5-17.
• Malpezzi, Stephen (2003), Hedonic Pricing Models: A Selective and Applied Review, in: T.O’Sullivan and K. Gibb (Eds) Housing Economics and Public Policy, 67–89, Malden, MA: Blackwell Science, https://doi.org/10.1002/9780470690680.ch5
• Nguyen, Nghiep; Cripps, Al (2001), “Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks”, Journal of Real Estate Research, Vol. 22, No. 3: 313-336.
• Niels Landwehr; Mark Halland, Eibe Frank (2005), “Logistic Model Trees”, Machine Learning, Vol. 59 Issue 1-2: 161-205,
• Önder, Kübra (2017), Current Approaches in Education and Economics, (Ed.)Hasan GÖKSU, “Comparative Analysis of The FactorsThat Affect House Prices Using Hedonic Pricing Model And Artificial Neural Network (ann) Method: A Case Study in Burdur Province”, Bölüm VI: 75-94, SRA Academic Publishing, United States of America.
• Pagourtzi, Elli; Assimakopoulos, Vassilis; Hatzichristos, Thomas; French, Nick (2003), Real Estate Appraisal: A review of Valuation Methods”, Journal of Property Investment & Finance, Vol. 21, No. 4: 383-401.
• Pierluigi, Morano; Francesco, Tajani (2013), “Bare ownership evaluation. Hedonic price model vs. artificial neural network, International Journal of Business Intelligence and Data Mining, Vol. 8, Issue 4: 340-362. https://doi.org/10.1504/IJBIDM.2013.059263
• Ratcford, Brian T. (1975), “The New Economic Theory of Consumer Behavior: An Interpretive Essay”, Journal of Con-sumer Research, Vol. 2, issue 2: 65-75.
• Revenue Administration (GIB) (2017), Aşınma Paylarına İlişkin Oranlar Cetveli, https:// intvd.gib. gov.tr/ 2014_Emlak_Arsa/pdf/AsinmaPaylarinaIliskinCetvel.pdf, (Erişim: 02.12.2017)
• Rosen, Sherwin (1974), “Hedonic Prices and Implicit Markets: Product Differentiation In Pure Competition”, Journal of Political Economy, Vol. 82, No. 1: 35-55, http://agecon2.tamu.edu/people/faculty/cappsoral/agec%20635/Readings/Hedonic%20Prices%20and20Implicit%20Markets%20Product%20Differentiation%20in%20Pure%20Competition.pdf, (Erişim: 02.01.2018).
• Selim, Hasan (2009), “Determinants of Houseprices in Turkey: Hedonic Regression Versus Artificial Neural Network”, Expert Systems with Applications, Vol. 36, Issue 2, Part 2: 2843-52, https://doi.org/10.1016/j.eswa.2008.01.044
• Shearer, Colin (2000), “The CRISP-DM Model: The New Blueprint for Data Mining”, Journal of Data Warehousing, Vol. 5, Konu, 4: 13-22.
• Sheppard, Stephen (1999), “Hedonic Analysis of Housing Markets”, Handbook of Regional and Urban Economics, Vol. 3: 1595-1635.
• Shimizu, Chihiro; Takatsuji, Hideoki; Ono, Hiroya; Nishimura, Kiyohiko G. (2010), “Structural and Temporal Changes in the Housing Market and Hedonic Housing Price Indices: A Case of the Previously Owned Condominium Market in the Tokyo Metropolitan 94 Area”, International Journal of Housing Markets and Analysis, Vol. 3, Issue, 4: 351-368, doi: 10.1108/17538271011080655
• Sivitanides, Petros (1997), “The Rent Adjustment Process and the Structural Vacancy Rate in the Commercial Real Estate Market”, Journal of Real Estate Research, Vol. 13, No. 2: 195-209.
• Steven, Peterson; Albert, Flanagan (2009), “Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal”, Journal of Real Estate Research, Vol. 31, No. 2: 147-164.
• Turgut, Hüseyin (2012), Veri Madenciliği Süreci Kullanılarak Alzheimer Hastalığı Teşhisine Yönelik Bir Uygulama, Yüksek Lisans Tezi, Isparta: SDÜ Fen Bilimleri Enstitüsü.
• TÜİK (2018a), Konut Satış İstatistikleri, http://www.tuik.gov.tr/PreTablo.do?alt_id=1056, (Erişim: 14.01.2018).
• TÜİK (2018b), Tüketim Harcaması Türleri, http://www.tuik.gov.tr/VeriBilgi.do?alt_id=1012, (Erişim: 14.01.2018).
• TÜİK, (2018c), Hanehalkı Bütçe Anketi Tüketim Harcaması Sonuçları, http://www.tuik.gov.tr/VeriBilgi.do?alt_id=1012, (Erişim: 14.01.2018).
• Visit, Limsombunchai; Christopher, Gan; Minsoo, Lee (2004), “House Price Prediction: Hedonic Price Model vs. Artifi-cial Neural Network”, American Journal of Applied Sciences, Vol. 1, No. 3: 193-201.
• Vries, Paul de; de Haan, Jan; der Wal, Erna van; Mari´en, Gust (2009), “A House Price Index Based on the SPAR Met-hod”, Journal of Housing Economics, Vol. 18, Konu, 3: 214-223, https://doi.org/10.1016/j.jhe.2009.07.002
• Wheaton, C. William, (1974), “A Bid Rent Approach to Housing Demand”, Journal of Urban Economics, Vol. 4, Issue 2: 200-217, https://dspace.mit.edu/bitstream/handle/1721.1/63384/bidrentapproacht00whea.pdf?sequence=1 (Erişim: 14.01.2018).
• Witten, Ian H; Frank, Eibe (2011), Data Mining: Practical Machine Learning Tools and Techniques (3rd edition), San Francisco: Morgan Kaufmann, United States of America.