Geographic and temporal variation in housing filtering rates

Time‐varying connectedness of metropolitan housing markets

“…(2019), and Liu et al. (2021) among many others. Compared to other commonly referenced house price indices, for example, the Federal Housing Finance Agency and S&P/Case‐Shiller indices, the Freddie Mac House Price Index includes appraisal values used for refinance transactions in addition to purchase transactions.…”

“…The construction of FMHPI is based upon a repeat transactions methodology, a common practice in housing and real estate research, which measures price appreciation by comparing the price of the same property over two or more transactions. The index has TA B L E 1 Summary statistics of monthly housing returns Housing returns are defined as the log difference of house price index and expressed in percent.extensively been used in housing, real estate, and urban research; see, for example,Akkoyun et al (2013),Karamon et al (2017),Christiansen et al (2019), andLiu et al (2021) among many others.…”

The heterogeneous impacts of widespread upzoning: Lessons from Auckland, New Zealand

“…Rosenthal (2014) used a constant quality repeat income index model and a structural model of housing demand to theorise filtering rates in the housing market as a function of depreciation and the change in house prices. Building on Rosenthal (2014), Liu et al (2022) estimated filtering rates at geographically and temporally disaggregated levels for owner-occupied properties and showed that filtering rates for owner-occupied, single-family houses vary widely across and within metropolitan statistical areas (MSAs) and across time in the United States.…”

The Role of Local Housing Policies in Preventing Displacement: A Literature Review

“…Rosenthal (2014) quantifies the rates of income filtering, finding that across the country, rental units transition to lowerincome households at an average rate of approximately 2.2% per year. Liu, McManus, and Yannopoulos (2020) find that filtering rates vary widely by geographic area and over time: while downward filtering can be rapid in certain cities, cities with serious affordable housing shortages, such as Los Angeles and Washington, DC, have experienced upward filtering-namely, housing going to higher-income people as it ages. Policies that encourage the creation of more housing can ease demand pressures and allow downward filtering to occur (Liu, McManus, and Yannopoulos 2020).…”

“…Liu, McManus, and Yannopoulos (2020) find that filtering rates vary widely by geographic area and over time: while downward filtering can be rapid in certain cities, cities with serious affordable housing shortages, such as Los Angeles and Washington, DC, have experienced upward filtering-namely, housing going to higher-income people as it ages. Policies that encourage the creation of more housing can ease demand pressures and allow downward filtering to occur (Liu, McManus, and Yannopoulos 2020). Some researchers, however, find that this is not always the case: though filtering rates are estimated at 1.5% annually for the Bay Area, rents are only declining at about 0.3% annually, a process that may be too slow to help lowincome households (Zuk and Chapple 2016).…”