Factors Determining the Demand for Affordable Housing

Abstract

Affordable housing has been defined as housing which is adequate in quality and location. In addition to this, it is deemed to be housing that is not so costly that it prevents its occupants from meeting their basic living needs. This study aims to develop a framework to facilitate affordable housing delivery. The data collection used was a survey questionnaire. The survey was administered to occupants within five high-rise buildings in Penang. The study also utilized a Kaiser-Meyer-Olkin measure of 0.518, and Bartlett’s test of sphericity of (x2 (210) = 10953.982, p<0.001). The two statistical test discovered that the major determinants affecting demand for affordable housing were crime rates, housing prices and down payments. The results also demonstrated that six factors were successfully constructed using a factor analysis and assigned as factors that determined the demand for affordable housing. The research will be useful to policy makers, urban planners, developers, and contractors.

Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm

“…However, in this study, the delay rate for medical decision-making in older patients with AIS was 74.76%, which is high. The median decision delay (480 min) in seeking treatment for older patients with AIS in this study is much higher than previous research results [ 15 – 17 , 30 , 31 ]. The main reason is the difference in the study population, which means that the delay in medical decision-making among older AIS patients is more severe than the median delay in decision-making among AIS patients in adult studies [ 15 , 17 , 30 , 31 ] or in rural areas [ 16 ].…”

“…Research reports have shown that the delay rate in medical decision-making ranges from 40.9 to 71.5%. A study conducted in Malaysia showed that the delay rate in medical decision-making for patients with AIS in hospitals was 54.9% [ 15 ]. Zhang et al investigated the delay status of patients with AIS in rural areas and found that the delay rate for this group of patients was 71.5% [ 16 ].…”

“…To gather disease information, we collected information including data on participants’ symptoms, National Institutes of Health Stroke Scale (NIHSS) score, onset time, location, first decision to seek medical attention. Whether a patient presents a medical decision-making delay was determined based on whether the time interval between the onset of symptoms and the initial decision to seek medical exceeded 1 h [ 15 , 16 ].…”

“…However, older patients with AIS do not always follow the best treatment plan [ 14 ] and often face delays in medical decision-making. This delay is characterised as a duration of more 1 h between the onset of discomfort symptoms to the patient’s initial decision to seek medical help [ 15 , 16 ]. This delay represents over 50% of the total time delay and is a stage that has not yet seen effective improvement.…”

Explainable housing price prediction with determinant analysis

“…This causes complications for the sellers and buyers to determine the optimal price for the property. To date, there is still limited number of studies to identify the main factors that contribute to housing price prediction (Lim et al, 2018).…”