Homelessness Prevention Simulation:
Thesis: Home buyers today buy 100% of a home, and borrow money through a mortgage to be able to afford to do so. But this purchase masks the fact that they are buying two things at the same time- 100% of the future sale price of a home (so-called appreciation value), and 100% of the usage rights of that home while they are living there (so-called use value). If people could buy only the percentage of appreciation value that they can afford, and still secure stable access to a home, half of homelessness in dense, coastal urban regions such as the Bay Area, New York, Seattle could be prevented. Stanford University has piloted such a model for decades by allowing professors to buy 100% of the use value of their off-campus home and 50% of the appreciation value of their home. This program has allowed individual professors to have asset diversification, access to housing, and an affordable share of the growth in the appreciating market value of their home. Through this program, Stanford University has created access to housing for professors without grant dollars or housing tax credits in a difficult housing market. Stanford’s endowment has earned returns on this investment comparable to the rest of Stanford endowment investments. The extension of this kind of alternative financing– investing in a portion of appreciation value per home– could make possible zero-subsidy affordable housing for preventing homelessness and reducing super-commuting.
In the Bay Area, jobs and housing creation are unbalanced. For the housing demand to be met, an added minimum of 275,000 homes are needed, available at price levels that neither impoverish nor displace working families. Until those homes are built, what solution might bring families with moderate incomes access to existing housing stock? The solution is to provide one part of our housing need solution by using a financial investment model based on the future appreciation of homes.