Banking sectors have allocated funds for micro and small-scale enterprises’ financing needs. However, the absorption of this fund is still considered low, particularly among the micro- and smallscale processing industries. The present study hence applied the multinomial logistic regression to understand the small industries’ loan decisions. The data were obtained from the 2019 Micro and Small Industry Survey and Financial Institution Statistics. It was found that income and profit did not exhibit a significant effect in both models and regions. However, the interest rate was found to have a positive effect in both models and regions, contradicting the Loanable Fund Theory. Collateral exhibited a positive effect in each region only in the first model. Meanwhile, other variables like age, financial record, business course, cooperative membership, business assistance (i.e., cooperative, noncooperative, and subsidized credit), internet access, and partnership exhibited different effects on small industries’ loan decisions in each model and region.

Housing is one of the human needs that must be met. Many ways are used to get the required residence (house). One of them is by taking a housing loan. Various factors can become obstacles in taking housing loans. This study used logistic regression – Logit and took a sample of 514 households in North Kalimantan Province. This research will examine the influence of economic, social, demographic, and geographic factors in household decisions to take housing loans. The results showed that household income, interest rates, HOH employment status, HOH age, and household location had a significant positive effect. In contrast, income per capita and marital status had a significant negative effect. Meanwhile, the number of HM working had no significant effect. Of the variables used, the household location variable influences a household’s decision to take housing loans with an OR of 3.62.