Immobility of housing along with seasonal, cyclical and other fluctuations may lead to excess supply or excess demand situations. Communities may either be gaining or losing population in particular income groups too rapidly for housing starts or housing demolitions to keep up. Large plant closings could result in homeowners finding the market unable to support sales at prices adequate to cover their investments. In this case, homeowners may postpone sales especially when markets appear temporarily depressed, to await a rebound in demand. Conversely, an unexpectedly large new plant may bring more workers to an area than existing housing stocks can accommodate. Truncated observations occur when individual buyers or sellers are significantly less informed or misinformed about an area's home prices. Moreover, in an effort to avoid capital gains taxes and given current tax laws, there is an incentive for families from high price areas to have a tendency to overpay in low price areas. This research combines and applies three econometric methods to estimate the regression models that predict the transaction prices of the houses in Bangkok's housing market. First, the hedonic housing price model is applied as the housing demand model. Second, the frontier production function model is developed as the housing supply model. Third, the switching regression model is employed as the housing market model. These three models are combined together added the truncated reservation price into the model. The new housing switching regression model with truncated distribution reservation prices allows us to simultaneously estimate and determine: (i) the potential transaction price of the house; (ii) the qualitative units of the house; (iii) the qualitative price per unit of the house; (iv) bargaining power; (v) sellers' and buyers' reservation prices; and (vi) the output at technically, allocatively, and overall inefficiency ratios of the housing market. The estimated results indicated that the housing switching regression model with truncated distribution reservation prices also provides better fitted regression lines than other models.