An integrated framework for affordable housing demand projection and site selection

9th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing

地球科学;计算机科学

Adi Maimun, N.H.^1 ; Ismail, S.^2 ; Junainah, M.^3 ; Razali, M.N.^4 ; Tarmidi, M.Z.^5 ; Idris, N.H.^5

Centre for Real Estate Studies, Institute for Smart Infrastructure and Innovative Construction, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Johor, Johor Bahru

81310, Malaysia^1

Faculty of Architecture and Ekistics, Universiti Malaysia Kelantan, Malaysia^2

Faculty of Architecture, Planning and Surveying, Universiti Teknologi MARA Perak Branch, Seri Iskandar Campus, Seri Iskandar, Perak

32610, Malaysia^3

Department of Real Estate, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Johor, Johor Bahru

81310, Malaysia^4

Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Johor, Johor Bahru

81310, Malaysia^5

关键词: Affordable housing policies;    Analysis and modeling;    Artificial neural network models;    Estimation techniques;    Integrated frameworks;    Location estimation;    Malaysian governments;    Modelling framework;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/169/1/012094/pdf
DOI  :  10.1088/1755-1315/169/1/012094

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Highly priced properties cause affordability problems among low and middle-income buyers. To overcome this, the Malaysian government introduces affordable housing through National Urbanisation Policy, National Physical Plan, National Housing Policy, and Eleventh Malaysia Plan. Whilst having good market response, some areas experience either shortage or surplus of houses reflecting ineffective affordable housing policies. Inappropriate estimation technique and aggregate location estimations limit the accuracy and usability of demand estimations. Thus, this research aims to establish a framework to estimate local demands for affordable housing. This study selects and reviews the theoretical and modelling framework of Artificial Neural Network Model (ANN) due to its superior performance in forecasting demand. The ANN theoretical and modelling framework guides the modelling process, which includes data collection and preparation, model development, data analysis and model evaluation. Potential sites for affordable housing development identified from the model's coefficients are visualised spatially through Geographic Information System (GIS). Localised housing demand forecasts are highly beneficial for policy-makers and housing developers to allocate the number of supplies across locations. This allows maximum take-up rate for affordable housing, avoids supply and demand mismatch and thus achieving the national housing policy agenda.