This study critically examines the relationship between Total Quality Management (TQM) and Service Quality (SQ) within Dubai’s housing sector, with a specific focus on the moderating influence of blockchain technology (BT) in this relationship. Employing a quantitative approach grounded in a deductive research strategy and positivist epistemology, data were gathered from a sample of industry professionals and subjected to rigorous analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that the deliberate deployment of TQM methodologies leads to significant improvements in SQ metrics, and the catalytic role of BT further enhances these service quality improvements. The study highlights the transformative potential of BT in recalibrating conventional paradigms of service delivery within the housing sector. Specifically, the analysis reveals that BT plays a pivotal moderating role in the relationship between TQM practices and SQ outcomes, thereby enriching our comprehension of the intricate interplay between these constructs. The study concludes by furnishing nuanced insights into the multifaceted dynamics shaping SQ within the housing sector, while also delineating avenues for future inquiry.

Governments intervene in the housing market via implementing various monetary, fiscal, foreign exchange and credit policies. By this, the housing market undergoes cycles of boom and bust as well as significant swings in value added and housing prices. Therefore, the main goal of this research is to consider the effect of the government’s change on the monetary and financial policy’s impact on the business cycles of the housing sector during the period of 1978–2020. On the other hand, we estimate the impact of monetary and fiscal policies on housing business cycles concerning government’s change. To calculate housing business cycles (boom and busts), the housing value added were initially de-trended using the Hodrick–Prescott filter. This paper takes a novel use of the threshold regression model with government’s change as threshold variable. According to the study’s findings, there are three threshold effects (two threshold levels or three regimes) of monetary and fiscal policy on housing business cycles. For instance, the money supply coefficient in the first regime was −1.68, indicating that the effect of monetary policy in this regime is countercyclical. in the second and third regimes, it was 0.19 and 0.03, respectively; indicating its alignment with the housing business cycle. Regarding the estimated models, we may derive several interesting conclusions. In first regime, the money supply is countercyclical and government expenditure is pro-cyclical. This means that monetary policy exacerbates recession and fiscal policy weakens it. in the second and third regimes, the money supply is pro-cyclical and government expenditure is countercyclical. As a result, while formulating their monetary policies, governments should give the housing sector more consideration. Additionally, when putting this policy into practice, the housing sector has to be carefully examined.