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Abd Jamil, A H and Fathi, M S (2018) Contractual challenges for BIM-based construction projects: a systematic review. Built Environment Project and Asset Management, 8(04), 372–85.

Adefila, A, Abuzeinab, A, Whitehead, T and Oyinlola, M (2020) Bottle house: utilising appreciative inquiry to develop a user acceptance model. Built Environment Project and Asset Management, 10(04), 567–83.

Al-Gahtani, K, Alsulaihi, I, Ali, M and Marzouk, M (2017) Production of green concrete using recycled waste aggregate and byproducts. Built Environment Project and Asset Management, 7(04), 413-25.

Amadi, C, Carrillo, P and Tuuli, M (2018) Stakeholder management in PPP projects: external stakeholders’ perspective. Built Environment Project and Asset Management, 8(04), 403–14.

Carrière, S, Weigend Rodríguez, R, Pey, P, Pomponi, F and Ramakrishna, S (2020) Circular cities: the case of Singapore. Built Environment Project and Asset Management, 10(04), 491–507.

Crippa, J, Araujo, A M, Bem, D, Ugaya, C M and Scheer, S (2020) A systematic review of BIM usage for life cycle impact assessment. Built Environment Project and Asset Management, 10(04), 603–18.

Farghaly, K, Abanda, F, Vidalakis, C and Wood, G (2019) BIM-linked data integration for asset management. Built Environment Project and Asset Management, 9(04), 489–502.

Hadiwattege, C, Senaratne, S, Sandanayake, Y and Fernando, N G (2018) Academic research in emerging knowledge-based economies. Built Environment Project and Asset Management, 8(04), 415–28.

Hettige, N, Perera, B A K S and Mallawaarachchi, H (2017) A framework for adopting green leasing in developing countries: The case of Sri Lanka. Built Environment Project and Asset Management, 7(04), 377-87.

Ikediashi, D I, Ogunlana, S O and Odesola, I A (2015) Service quality and user satisfaction of outsourced facilities management services in Nigeria’s public hospitals. Built Environment Project and Asset Management, 5(04), 363-79.

Jafari, A and Akhavian, R (2019) Driving forces for the US residential housing price: a predictive analysis. Built Environment Project and Asset Management, 9(04), 515–29.

  • Type: Journal Article
  • Keywords: Stepwise regression; Data analytics; Residential property; Hedonic pricing method; Housing prices; Predictive model;
  • ISBN/ISSN: 2044-124X
  • URL: https://doi.org/10.1108/BEPAM-07-2018-0100
  • Abstract:
    The purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of housing prices can be extremely useful in the built environment and real estate decision-making processes. Design/methodology/approach A data set of 13,771 houses is extracted from the 2013 American Housing Survey (AHS) data and used to develop a Hedonic Pricing Method (HPM). Besides, a data set of 22 houses in the city of San Francisco, CA is extracted from Redfin real estate brokerage database and used to test and validate the model. A correlation analysis is performed and a stepwise regression model is developed. Also, the best subsets regression model is selected to be used in HPM and a semi-log HPM is proposed to reduce the problem of heteroscedasticity. Findings Results show that the main driving force for housing transaction price in the USA is the square footage of the unit, followed by its location, and its number of bathrooms and bedrooms. The results also show that the impact of neighborhood characteristics (such as distance to open spaces and business centers) on the housing prices is not as strong as the impact of housing unit characteristics and location characteristics. Research limitations/implications An important limitation of this study is the lack of detailed housing attribute variables in the AHS data set. The accuracy of the prediction model could be increased by having a greater number of information regarding neighborhood and regional characteristics. Also, considering the macro business environment such as the inflation rate, the interest rates, the supply and demand for housing, and the unemployment rates, among others could increase the accuracy of the model. The authors hope that the presented study spurs additional research into this topic for further investigation. Practical implications The developed framework which is capable of predicting the driving forces of housing prices and predict the market values based on those factors could be useful in the built environment and real estate decision-making processes. Researchers can also build upon the developed framework to develop more sophisticated predictive models that benefit from a more diverse set of factors. Social implications Finally, predictive models of housing price can help develop user-friendly interfaces and mobile applications for home buyers to better evaluate their purchase choices. Originality/value Identification of the key driving forces that determine housing prices on real-world data from the 2013 AHS, and development of a prediction model for housing prices based on the studied data have made the presented research original and unique.

Jumas, D, Mohd-Rahim, F A, Zainon, N and Utama, W P (2018) Improving accuracy of conceptual cost estimation using MRA and ANFIS in Indonesian building projects. Built Environment Project and Asset Management, 8(04), 348–57.

Khallaf, R, Naderpajouh, N and Hastak, M (2018) A systematic approach to develop risk registry frameworks for complex projects. Built Environment Project and Asset Management, 8(04), 334–47.

Kim, K P and Park, K S (2016) Primary BIM dataset for refurbishing flood risk vulnerable housing in the UK. Built Environment Project and Asset Management, 6(04), 365-78.

Kumaraswamy, M, Wong, K K W and Chung, J (2017) Focusing megaproject strategies on sustainable best value of stakeholders. Built Environment Project and Asset Management, 7(04), 441-55.

Liu, C and Li, Y (2016) Measuring eco-roof mitigation on flash floods via gis simulation. Built Environment Project and Asset Management, 6(04), 415-27.

Madanayake, U H and Egbu, C (2019) Critical analysis for big data studies in construction: significant gaps in knowledge. Built Environment Project and Asset Management, 9(04), 530–47.

Manu, P, Mahamadu, A, Booth, C, Olomolaiye, P, Ibrahim, A D and Coker, A (2018) Assessment of procurement capacity challenges inhibiting public infrastructure procurement. Built Environment Project and Asset Management, 8(04), 386–402.

Marzouk, M and Enaba, M (2019) Analyzing project data in BIM with descriptive analytics to improve project performance. Built Environment Project and Asset Management, 9(04), 476–88.

Mitra, A and Munir, K (2019) Influence of Big Data in managing cyber assets. Built Environment Project and Asset Management, 9(04), 503–14.

Nguyen, T P and Chileshe, N (2015) Revisiting the construction project failure factors in Vietnam. Built Environment Project and Asset Management, 5(04), 398-416.

Nielsen, L-H K, Akanmu, A and Anumba, C J (2015) Comparative analysis of back-to-back subcontracts in the construction and telecommunications industries. Built Environment Project and Asset Management, 5(04), 446-60.

Ofori-Boadu, A N, Shofoluwe, M A, Owusu-Manu, D-G, Holt, G D and Edwards, D (2015) Analysis of US commercial buildings’ energy efficiency programs. Built Environment Project and Asset Management, 5(04), 349-62.

Okorafor, C, Emuze, F, Das, D, Awuzie, B O and Haupt, T (2020) An artefact for improving the delivery of building energy retrofit project in South Africa. Built Environment Project and Asset Management, 10(04), 619–35.

Olatunji, O A, Orundami, A O and Ogundare, O (2018) Causal relationship between material price fluctuation and project’s outturn costs. Built Environment Project and Asset Management, 8(04), 358–71.

Omotayo, T, Olanipekun, A, Obi, L and Boateng, P (2020) A systems thinking approach for incremental reduction of non-physical waste. Built Environment Project and Asset Management, 10(04), 509–28.

Osunsanmi, T O, Aigbavboa, C O, Emmanuel Oke, A and Liphadzi, M (2020) Appraisal of stakeholders' willingness to adopt construction 4.0 technologies for construction projects. Built Environment Project and Asset Management, 10(04), 547–65.

Oyewobi, L O, Windapo, A O and James, R O B (2015) An empirical analysis of construction organisations’ competitive strategies and performance. Built Environment Project and Asset Management, 5(04), 417-31.

Ozcan, D G (2017) An analytic network process model to examine LEED-certified buildings’ operational performance. Built Environment Project and Asset Management, 7(04), 366-76.

Pathirage, C and Al-Khaili, K (2016) Disaster vulnerability of Emirati energy sector and barriers to enhance resilience. Built Environment Project and Asset Management, 6(04), 403-14.

Ram, J, Afridi, N K and Khan, K A (2019) Adoption of Big Data analytics in construction: development of a conceptual model. Built Environment Project and Asset Management, 9(04), 564–79.

Rose, J and Jayawickrama, J (2016) Capacity building of institutions for disaster risk reduction: Learning from communities as first responders. Built Environment Project and Asset Management, 6(04), 391-402.

Seneviratne, K, Amaratunga, D and Haigh, R (2015) Post-conflict housing reconstruction: Exploring the challenges of addressing housing needs in Sri Lanka. Built Environment Project and Asset Management, 5(04), 432-45.

Waidyasekara, K G A S, De Silva, L and Rameezdeen, R (2017) Application of “r” principles to enhance the efficiency of water usage in construction sites. Built Environment Project and Asset Management, 7(04), 400-12.

Walimuni, P C, Samaraweera, A and De Silva, L (2017) Payment mechanisms for contractors for better environmental hazard controlling in road construction projects. Built Environment Project and Asset Management, 7(04), 426-40.

Wedawatta, G and Ingirige, B (2016) A conceptual framework for understanding resilience of construction SMEs to extreme weather events. Built Environment Project and Asset Management, 6(04), 428-43.

Wedawatta, G, Kulatunga, U, Amaratunga, D and Parvez, A (2016) Disaster risk reduction infrastructure requirements for south-western Bangladesh: Perspectives of local communities. Built Environment Project and Asset Management, 6(04), 379-90.

Weigend Rodríguez, R, Pomponi, F, Webster, K and D'Amico, B (2020) The future of the circular economy and the circular economy of the future. Built Environment Project and Asset Management, 10(04), 529–46.

Windapo, A O and Moghayedi, A (2020) Adoption of smart technologies and circular economy performance of buildings. Built Environment Project and Asset Management, 10(04), 585–601.

Yap, J Y L, Ho, C C and Ting, C (2019) A systematic review of the applications of multi-criteria decision-making methods in site selection problems. Built Environment Project and Asset Management, 9(04), 548–63.

Zeb, J (2017) An eco asset ontology towards effective eco asset management. Built Environment Project and Asset Management, 7(04), 388-99.

Zeb, J, Froese, T and Vanier, D (2015) An ontology-supported asset information integrator system in infrastructure management. Built Environment Project and Asset Management, 5(04), 380-97.

Zhao, X and Pan, W (2017) Co-productive interrelations between business model and zero-carbon building: A conceptual model. Built Environment Project and Asset Management, 7(04), 353-65.