This dissertation consists of three works that analyze real estate markets from macroeconomic perspectives. In the first essay, a Reverse Mortgage Loan (RML) allows senior homeowners to smooth consumption across time and generations in response to uninsurable shocks. Meanwhile, RML borrowers lose the opportunity to bequeath the whole equity of their home. Borrowing an RML is an intertemporal choice problem, which depends on various factors. This essay studies how intergenerational risk-sharing affects RML origination and intergenerational transfers during the housing boom and bust. Ibuild an overlapping generations model with one-sided altruism to explain how a parent strategically behaves to maximize a dynasty’s utility. I find that parents owning a relatively smaller home and scarce liquid assets are the principal borrowers of an RML. As children’s income increases, the RML take-up rate initially decreases and then increases. As the size of the bequest motive increases, the RML take-up rate decreases; however, more slowly during the recession.

In the second essay, open enrollment policies provide more public school options by allowing a student to transfer to a public school of her choice regardless of residency. This essay investigates the effect of open enrollment on housing prices and income inequality. I consider school districts in Arizona and North Carolina, as opposite extremes in enrollment policies. I find some evidence on the effect of open enrollment on housing prices and income inequality. In a state with open enrollment, housing prices increase with the number of better schools far from home. The Gini coefficient decreases with the quality of public education in a state with open enrollment. In an overlapping generations modelwith altruism, I examine how changes in the quality of public education, private school tuition, and transportation cost affect housing prices and income inequality across states.

The third essay studies how the composition of businesses of different qualities changes between a gentrifying and a non-gentrifying neighborhood in response to higher rent. I investigate how actively commercial gentrification is going on in some neighborhoods in Los Angeles, USA, using Yelp data. Then, I construct a search and matching model with heterogeneous neighborhoods, rents, and search friction. The model predicts a higher proportion of high-quality businesses and rents in a gentrifying neighborhood than a non-gentrifying neighborhood. Depending on whether high and low-quality goods are complements or substitutes, changes in the composition of businesses and rents showdifferent patterns between a gentrifying and a non-gentrifying neighborhood.