Sales rate forecasting of single-detached houses using artificial neural network technique

Kongkoon Tochaiwat; Patcharida Pultawee; Damrongsak Rinchumphu

doi:10.31181/dmame622023707

Authors

Kongkoon Tochaiwat Faculty of Architecture and Planning, Thammasat University, Pathumthani, 12121, Thailand https://orcid.org/0000-0002-4769-8181
Patcharida Pultawee Thammasat University Research Unit in Project Development and Innovation in Real Estate Business, Pathumthani, 12121, Thailand https://orcid.org/0000-0003-0918-5489
Damrongsak Rinchumphu Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand https://orcid.org/0000-0002-6037-4974

DOI:

https://doi.org/10.31181/dmame622023707

Keywords:

Prediction Model, Absorption Rate, Housing Project, Single-detached House, Artificial Neural Network

Abstract

Although the predicted number of sold units per month is one of the most necessary information to study the feasibility of detached-housing estate projects, traditional forecasting methods are limited. The research objective was to apply the Artificial Neural Network (ANN) technique to develop a sales rate forecasting model by compiling factors from an appropriate literature review. Then, 100 housing project data were collected from market research reports and the projects' websites and analyzed using the ANN technique. The results showed the ANN network with 16 input nodes from 10 factors: Selling price, Number of bathrooms, Number of bedrooms, Distance from the main road, Distance from the bus stop, Distance from the expressway, Distance from the metro station, Distance from the gas station, Distance from the shopping mall, and Project location zone. The acquired model had a Root Mean Square Error (RMSE) of ±6.296, and the slope and R2 values of the linear regression analysis between the forecasted rate and the actual rate were 0.620 and 0.571, respectively. The findings guide real estate developers and academia on the factors affecting the sales rate and provide a decision support model for investment and design of projects and confirm the potential of ANN in solving the problem with limited numbers of data.

Downloads

Download data is not yet available.

References

A.L.M. Media Properties. (2014). Control of common areas. Law Journal Newsletter. Https://Www.Lawjournalnewsletters.Com.

Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. https://doi.org/10.1016/j.heliyon.2018.e00938

Afonso, B., Melo, L., Oliveira, W., Sousa, S., & Berton, L. (2019). Housing prices prediction with a deep learning and random forest ensemble. Anais Do XVI Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2019), 389–400. https://doi.org/10.5753/eniac.2019.9300

Agency for Real Estate Affair. (2017). An in-depth look at bangkok and surrounding areas in the middle of 2017. Agency for Real Estate Affair.

Agency for Real Estate Affair. (2011). An in-depth look at bangkok and surrounding areas in the middle of 2011. Agency for Real Estate Affair.

Agency for Real Estate Affair. (2014). An in-depth look at bangkok and surrounding areas in the middle of 2014. Agency for Real Estate Affair.

Agency for Real Estate Affair. (2021). Real Estate Market Analysis. Www.Trebs.Ac.Th/Th/News_detail.Php?Nid=163. .

Ariyawansa, R. (2010). An empirical study of consumer behavior in housing market in Colombo. Built-Environment Sri Lanka, 8(1), 11–19. https://doi.org/10.4038/besl.v8i1.1906

Aryani, N. ., & Tu, K. . (2017). The factors affecting the home buying decisions related to house physical characteristics in a middle-up estate in surabaya, Indonesia. Architecture Science, 15, 1–16. https://doi.org/ 10.3966/221915772017060015001

Çelik, U., & Basarir, C. (2017). The prediction of precious metal prices via artificial neural network by using rapidminer. Alphanumeric Journal, 5(1), 45–45. https://doi.org/10.17093/alphanumeric.290381

Chia, J., Huran, A., Kasim, A. W. ., Martin, D., & Kepal, N. (2016). Understanding factors that influence house purchase intention among consumers in kota kinabalu: An application of buyer behavior model theoryI. Journal of Technology Management and Business, 3(2), 94–110.

Dechkamfoo, C., Sitthikankun, S., Kridakorn Na Ayutthaya, T., Manokeaw, S., Timprae, W., Tepweerakun, S., Tengtrairat, N., Aryupong, C., Jitsangiam, P., & Rinchumphu, D. (2022). Impact of rainfall-induced landslide susceptibility risk on mountain roadside in northern thailand. Infrastructures, 7(2), 17. https://doi.org/10.3390/infrastructures7020017

Elsinga, M., & Hoekstra, J. (2005). Homeownership and housing satisfaction. Journal of Housing and the Built Environment, 20(4), 401–424. https://doi.org/10.1007/s10901-005-9023-4

Figueiredo Filho, D. B., Silva Júnior, J. A., & Rocha, E. C. (2011). What is R2 all about? Leviathan (São Paulo), 3, 60. https://doi.org/10.11606/issn.2237-4485.lev.2011.132282

Geetha, A., & Nasira, G. M. (2014). Data mining for meteorological applications: Decision trees for modeling rainfall prediction. 2014 IEEE International Conference on Computational Intelligence and Computing Research, 1–4. https://doi.org/10.1109/ICCIC.2014.7238481

Ghumare, P. N., Chauhan, K. A., & Yadav, S. K. M. (2019). Housing attributes affecting buyers in India. International Journal of Housing Markets and Analysis, 13(4), 533–552. https://doi.org/10.1108/IJHMA-08-2019-0081

Gibler, K., & Nelson, S. (2003). Consumer behavior applications to real estate education. Journal of Real Estate Practice and Education, 6(1), 63–83. https://doi.org/10.1080/10835547.2003.12091585

Henilane, I. (2016). Housing concept and analysis of housing classification. Baltic Journal of Real Estate Economics and Construction Management, 4(1), 168–179. https://doi.org/10.1515/bjreecm-2016-0013

Hsu, C., Goh, J., & Chang, P. (2012). Development of decision support system for house evaluation and purchasing. International Journal of Computer and Information Engineering, 6(5), 574–579.

Ismail, H., & Shaari, S. M. (2020). The location, house, or neighbourhood choice preferences among malaysian housing generations. Journal of Surveying, Construction & Property, 11(2), 64–74. https://doi.org/10.22452/jscp.sp2020no1.6

Kamolsuksatit, S. (2020). Housing estate juristic person law and public property management, Public utilities. The Academic Journal: Faculty of Humanities and Social Sciences Nakhonsawan Rajabhat University, 6(2), 249–260.

Khan, P.A.M., Azmi A., Juhari N., N., K., Daud, S. Z., & Rahman, T. (2017). Housing preference for first time home buyer in malaysia. International Journal of Real Estate Studies, 11(2), 1–6.

Koklic, M. K., & Vida, I. (2006). An examination of a strategic household purchase: consumer home buying behavior. In N.A. - Advances in Consumer Research, 33, 288–289.

Kumar, Y., & Khandelwal, U. (2018). Factors affecting buying behaviour in the purchase of residential property: A factor analysis approach. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3481597

Land & Houses. (2016). The Common Area Must Always Be Available. Https://Www.Lh.Co.Th/Th/Lh-Living-Concept/Tips. .

Laszlo, K., & Ghous, H. (2020). Efficiency comparison of python and rapidminer. Multidiszciplináris Tudományok, 10(3), 212–220. https://doi.org/10.35925/j.multi.2020.3.26

Li, Y., Zhu, D., Zhao, J., Zheng, X., & Zhang, L. (2020). Effect of the housing purchase restriction policy on the real estate market: evidence from a typical suburb of beijing, China. Land Use Policy, 94, 104528. https://doi.org/10.1016/j.landusepol.2020.104528

Lim, W. T., Wang, L., Wang, Y., & Chang, Q. (2016). Housing price prediction using neural networks. 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 518–522. https://doi.org/10.1109/FSKD.2016.7603227

Mang, J. S., Zainal, R., & Radzuan, I. S. M. (2018). Influence of location on home buyers’ purchase decision, 2016 (1), 020078. https://doi.org/10.1063/1.5055480

Maoludvo, F. T., & Apriamingsih, A. (2015). Factors influencing consumer buying intention for housing unit in depok. Journal of Business And Management, 4(4), 484–493.

Matel, E., Vahdatikhaki, F., Hosseinyalamdary, S., Evers, T., & Voordijk, H. (2022). An artificial neural network approach for cost estimation of engineering services. International Journal of Construction Management, 22(7), 1274–1287. https://doi.org/10.1080/15623599.2019.1692400

Morano, P., Tajani, F., & Torre, C. M. (2015). Artificial intelligence in property valuations. An application of artificial neural networks to housing appraisal. Advances in Environmental Science and Energy Planning, 23–29.

Nahmens, I., & Ikuma, L. H. (2009). Discovering the variables that influence new home-buyer service satisfaction. International Journal of Consumer Studies, 33(5), 581–590. https://doi.org/10.1111/j.1470-6431.2009.00801.x

Nasar, J. L., & Elmer, J. R. (2016). Homeowner and homebuyer impressions of visitable features. Disability and Health Journal, 9(1), 108–117. https://doi.org/10.1016/j.dhjo.2015.08.012

Nastasi, B. K., & Schensul, S. L. (2005). Contributions of qualitative research to the validity of intervention research. Journal of School Psychology, 43(3), 177–195. https://doi.org/10.1016/j.jsp.2005.04.003

Nghiep, N., & Al, C. (2001). Predicting housing value: A comparison of multiple regression analysis and artificial neural networks. Journal of Real Estate Research, 22(3), 313–336. https://doi.org/10.1080/10835547.2001.12091068

Nuntanach, D. (2021). Look at the Sales of Houses-condominiums for the Half of 2021. Https://Thinkofliving.Com.

Opoku, R. A., & Abdul-Muhmin, A. G. (2010). Housing preferences and attribute importance among low-income consumers in Saudi Arabia. Habitat International, 34(2), 219–227. https://doi.org/10.1016/j.habitatint.2009.09.006

Panyafong, A., Neamsorn, N., & Chaichana, C. (2020). Heat load estimation using artificial neural network. Energy Reports, 6, 742–747. https://doi.org/10.1016/j.egyr.2019.11.149

Pornchokchai, S. (2019). International Real Estate Markets Surveyed (2nd ed.). Thai Appraisal Foundation.

Project Management Institute. (2017). A Guide to the Project Management Body of Knowledge (PMBOK Guide) (6th ed.). Project Man-agement Institute, Inc. .

Pultawee, P., & Tochaiwat, K. (2022). Classification of common area preference factors affecting subdivision project customers’ willingness to pay. ABAC Journal, 42(3), 87–98. https://doi.org/10.14456/abacj.2022.22

Quester, P., Neal, C., Pettigrew, S., Grimmer, M.R., Davis, T., & Hawkins, D. (2007). Consumer Behaviour: Implications for Marketing Strategy (5th ed.). McGraw-Hill Education.

Rahman, S. N. A., Maimun, N. H. A., Najib, M., Razali, M., & Ismail, S. (2019). The artificial neural network model (ANN) for malaysian housing market analysis. Planning Malaysia Journal, 17(9). https://doi.org/10.21837/pmjournal.v17.i9.581

Ranjan, P., Sahu, T. K., Bhushan, R., Yamijala, S. S., Late, D. J., Kumar, P., & Vinu, A. (2019). Freestanding borophene and Its hybrids. Advanced Materials, 31(27), 1900353. https://doi.org/10.1002/adma.201900353

Real Estate Information Center [REIC]. (2018). The Situation of the Housing Market in Bangkok - Surrounding Areas in the 3rd Quarter of 2018 and Trends Year 2019. Www.Reic.or.Th.

Real Estate Information Center [REIC]. (2020). Surviving Stabilizing Sustaining Real Estate Industry 2020. Www.Reic.or.Th/Upload/REICAnnualReport2020_896_1628142428_44149.Pdf.

Real Estate Information Center [REIC]. (2022). The Situation of the Housing Market in Bangkok – POST-COVID. Www.Reic.or.Th/News/RealEstate/455280.

Rico-Juan, J. R., & De La Paz, P. T. (2021). Machine learning with explainability or spatial hedonics tools? An analysis of the asking prices in the housing market in Alicante, Spain. Expert Systems with Applications, 171, 114590. https://doi.org/10.1016/j.eswa.2021.114590

Rinchumphu, D., Eves, C., & Susilawati, C. (2013). Brand value of property in bangkok metropolitan region (BMR), Thailand. International Real Estate Review, 16(3), 296–322.

Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling. In Research Methodologies, Innovations and Philosophies in Software Systems Engineering and Information Systems (pp. 193–221). IGI Global. https://doi.org/10.4018/978-1-4666-0179-6.ch010

Salisu, U. O., Odulaja, A. O., Ogunseye, N. O., Fasina, S. O., & Okunubi, S. A. (2019). residents’ satisfaction with public housing in Lagos, Nigeria. Ghana Journal of Geography, 11(1), 180–200. https://dx.doi.org/10.4314/gjg.v11i1.11

Sbakhi, B., Esa, M., & Mohd, W. M. S. (2018). Investigation study towards consumer behavior and purchasing decisions. International Journal of Supply Chain Management, 7, 559–563. https://doi.org/10.59160/ijscm.v7i5.2398

Suttiwongpan, C., Tochaiwat, K., & Naksuksakul, S. (2019). Influence of designs following green assessment criteria on decision to buy houses in housing projects: Thailand’s ecovillage. ABAC Journal, 39(4), 1–15.

Temür, A. S., Akgün, M., & Temür, G. (2019). Predicting housing sales in Turkey using ARIMA, LSTM and hybrid model. Journal of Business Economics and Management, 20(5), 920–938. https://doi.org/10.3846/jbem.2019.10190

Tochaiwat, K., Kulintonprasert, W., Jiraprasertkun, C., & Yang, C. Y. (2018). Influence of master plan designs on housing project purchase decisions in Bangkok metropolitan areas. International Journal of Building, Urban, Interior and Landscape Technology (BUILT), 11, 43–54.

Tochaiwat, K. (2020). Subdivision Project Development. Thammasat Press.

Wang, C. (2013). Family house-purchase decision model based on analytic hierarchy process. Applied Mechanics and Materials, 423–426, 2973–2976. https://doi.org/10.4028/www.scientific.net/AMM.423-426.2973

Wattanachai, P., Sundaranaga, C., Ayutthaya, T. K. N., Phichetkunbodee, N., & Rinchumphu, D. (2021). Study of universal thermal comfort index in hosing estate public space in Bangkok, Thailand. Journal of Design and Built Environment, 21(2), 10–18. https://doi.org/10.22452/jdbe.vol21no2.2

Wen, H., Xiao, Y., & Hui, E. C. M. (2019). Quantile effect of educational facilities on housing price: Do homebuyers of higher-priced housing pay more for educational resources? Cities, 90, 100–112. https://doi.org/10.1016/j.cities.2019.01.019

Zainun, N. Y. B., Rahman, I. A., & Eftekhari, M. (2010). Forecasting low-cost housing demand in Johor Bahru, Malaysia using artificial neural networks (ANN). Journal of Mathematics Research, 2(1). https://doi.org/10.5539/jmr.v2n1p14

Zeng, R. (2013). Attributes influencing home buyers’ purchase decisions : a quantitative study of the Wuhan residential housing market. Business Administration Thesis, Southern Cross Business School.