Abstract
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.
Highlights
The process of urbanization creates a high demand for houses
Real Estate Applications of Artificial Neural Network Model (ANN) The real estate applications of ANN began in the 1990s
Phase 4: Model evaluation and spatial visualisation At the fourth stage, the ANN model results are evaluated through statistical tests such as Root Mean Squared Error (RMSE), Mean Absolute Deviation Error (MAD), and Mean Absolute Percentage Error (MAPE)
Summary
The process of urbanization creates a high demand for houses This has caused a rapid increase in house prices including the low and middle cost houses [1]. Apart from high prices, poor take up rate in this property segment include inconvenient location, lack of accessibility, unsuitable home design and the inability of buyers to secure end-financing [6][7] In response to this severe housing situation, the Malaysian government and its agencies have launched various affordable housing policies. A forecast of housing demand using ANN model by these researchers suggest good predictive performance These studies were carried out based on a very general, aggregated data sampling area such as Johor Bahru [11][13] and Batu Pahat [12]. With demand estimations in place, the suppliers can react accurately to the actual market demand and avoiding a supply-demand mismatch
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