There is a serious lack of empirical evidence on household credit demand in Zambia. This has led to an information gap in terms of evidence-based policy that would aid policy makers in the formulation of possible policy intervention to help stimulate and sustain household credit. Therefore, this dissertation attempts to partially fulfill this gap by analyzing the determinants of household credit demand in Zambia. Using a sample size of 20,000 households from the Living Conditions Monitoring Survey (LCMS) V of 2006, a probit model was used to predict the probability of borrowing due to the binary nature of the dependent variable. In addition, a subsequent tobit procedure was implemented to take into account the potential selectivity bias that arises due to the non-random choice of borrowing households. The total amount of borrowing per household, taken as a proxy of credit demand, is the dependent variable. Various important explanatory variables that influence credit demand were regressed against total borrowing. The explanatory variables include the size of the household, gender, age, education, total household expenditure, remittances received and residence. The discrete choice models that control for potential endogeneity and selectivity bias showed that all the explanatory variables were significant correlates of credit demand. These results reveal that the likelihood of credit demand will be higher with an increase in the size of the household, age and education. Furthermore, remittances received and residence in an urban area also increases the probability of borrowing. On the other hand, the probability of borrowing declines when a household is headed by a female or has low levels of expenditure. Based on these results, it is recommended that policy makers promote basic infrastructure for financial sector development in rural areas and focus on education policy that will enhance employment opportunities and individuals’ future income. They should also form credit schemes for the elderly and implement policies that are gender sensitive to gender inequalities in the financial market.