Globally affordable housing provision is a major challenge for the Govts, particularly in the developing countries. Population growth and urbanization are key factors behind the growing demand for affordable housing. Pakistan having 191.71million population is facing acute housing shortage of about 09million units with annual demand of 0.7million units with growing housing supply-demand gap. The housing deficit for low income households is about 4.5 million units, with annual addition of 150000 units. Most of the housing projects developed by private developers meant for higher profits and targets higher & higher middle income groups. Murree city has been selected as case study. The data was collected through primary sources by household questionnaire and stakeholder’s interviews, while secondary data was collected through public depts. Reports, journal articles etc. The data analysis was done using SPSS software and correlation test was applied. SWOT analysis and need assessment was carried out for the provision of affordable housing. Murree city has been selected as case study. The data was collected through primary sources by household questionnaire and stakeholder’s interviews, while secondary data was collected through public depts.  Reports, journal articles etc. The data analysis was done using SPSS software and correlation test was applied. SWOT analysis and need assessment was carried out for the provision of affordable housing. The present population of Murree is 28500 persons and housing units are 3000, with housing deficit of 1700 units. Most of the households are living in overcrowded, low quality housing conditions, with small size units, higher household size, low education levels and substandard infrastructure facilities. The data analysis on economic variables revealed that, most of the respondents are unable to save for upgrading and construction of their units with low income levels. The land and unit cost is too high for them to afford. The correlation analysis indicates that affordability has highly significant relationship with socioeconomic variables i.e., plot/unit size, plot/unit cost, and distance to job. Moreover household size, income level, and non-housing expenses.