Ordinal regression

Regression analysis

Variables

Construct (python library)

Mathematics

Ordered logit

Statistics

Logistic regression

Econometrics

Psychology

Economics

Programming language

Computer science

Growth and Change

Residential satisfaction is not only an important component of individuals' quality of life but also determines the way they respond to residential environment. An understanding of the factors that facilitate a satisfied or dissatisfied response can play a critical part in making successful housing policies. This study reinvestigates the effects of housing, neighborhood, and household characteristics on individuals' satisfaction with both dwelling and neighborhood, in order to reconcile the inconsistencies in the previous research. The empirical analysis uses data drawn from the American Housing Survey (AHS) and ordered logit models (OLM). OLM is more appropriate than the widely‐used regression technique in such analysis due to the ordinal nature of the dependent variables representing satisfaction. The results show that residential satisfaction is a complex construct, affected by a variety of environmental and socio‐demographic variables. While the actual effects of the variables by and large confirm earlier findings in the literature, significant differences between the results from the OLM and regression models were found. This indicates that regression models should be used with caution and their results accepted with a grain of salt.

Determinants of Residential Satisfaction: Ordered Logit vs. Regression Models