Figure 1: Mortgage Rates and Residential Mobility
(a) Rates
on Outstanding Mortgages vs. Market Rate
(b) Median Rate Gap
vs. Mortgagor Mobility Rates
Note: The left panel plots
median and inter-quartile range of rates on outstanding mortgages (blue)
compared with market rate for originating a purchase mortgage (red).
Rate increases in 2022 have pushed current mortgage borrowers
significantly out of the money for refinancing or moving. The right
panel plots median rate gap–the difference between a borrower’s current
interest rate and prevailing purchase mortgage rates for this type of
borrower–along with the seasonally-adjusted monthly probability of a
mortgage borrower moving. As rate gaps fell following interest rate
increases in 2022, mobility rates also declined sharply.
Source: ICE, McDash and Equifax Credit Risk Insight Servicing Data
(CRISM), Freddie Mac Primary Mortgage Market Survey (PMMS)
Figure 1 left panel plots median (solid blue line) and inter-quartile range of rates on outstanding mortgages (blue shaded region) compared with market rate for originating a purchase mortgage (solid red line). The median and interquartile ranges of outstanding mortgages have fallen gradually over the 2008 to 2023 period plotted, converging towards market rates on new mortgages, which have fluctuated between 3.5 and 5 percent. Outside of brief periods during the Taper Tantrum in 2017, market rates remained below rates on existing loans. Sharp increases in market rates in 2022 led the market rate to rise significantly above rates on existing loans, pushing current mortgage borrowers significantly out of the money for refinancing or moving.
Figure 1 right panel plots median rate gap (red dashed line)–the difference between a borrower’s current interest rate and prevailing purchase mortgage rates for this type of borrower (shown in left panel)–along with the seasonally-adjusted monthly probability of a mortgage borrower moving (blue solid line). As discussed previously, rate gaps gradually declined between 2008 and 2018 as households refinanced into lower rates before stepping upwards in 2019 amid declining market rates. Meanwhile, monthly mobility rates fluctuated between 0.50 percent and 0.65 percent between 2008 and 2020 before spiking up during the pandemic (March 2020 through early 2021). As rate gaps fell following interest rate increases in 2022, mobility rates also declined sharply.
Figure 2: Evolution of the Rate Gap Distribution, Dec. 2021 - Dec.
2022
Figure 2 plots three densities corresponding to the distribution of rate gaps in December 2021 (dotted blue), June 2022 (solid red), and December 2022 (dashed black). In December 2021, the mass of rate gaps was predominantly between rate gaps of -1 and +3, with the mode centered around +1. Only a minority of loans were below 0. As market mortgage rates began to rise in 2022, the distribution shifted left. In June 2022, the distribution lay primarily between -3 and +1 and by the end of 2022, the distribution had shifted further to be between -4 and 0, with almost all borrowers in the negative range.
Figure 3: Mobility by Rate Gap, 2009-2023
Note: This figure
plots estimated monthly probability of moving conditional on borrower’s
rate gap using estimated regression coefficients from Equation (2)
evaluated at mean of control variables. Vertical bars mark 95 percent
confidence bands using standard errors clustered by origination-month
cohort. Alternate specifications are shown in Appendix Figure A.1 and A.2 in Appendix
A.
Source: Authors’
calculations using ICE/McDash and Equifax CRISM, Cotality deeds
data
Figure 3 plots estimated monthly probability of moving conditional on borrower’s rate gap using estimated regression coefficients from Equation (2) evaluated at mean of control variables (red dots). The x-axis displays the rate gap, and the y-axis displays the annualized move probability conditional on rate gap. Each dot reflects one of 30 equally spaced quantiles of the rate gap distribution. Vertical red bars mark 95 percent confidence bands using standard errors clustered by origination-month cohort. The relationship appears relatively flat in the negative rate gap range, rising from a 6 percent annualized move hazard at a rate gap just above -3, rising to about a 7 percent annual hazard at rate gaps just above 0. The slope becomes considerably steeper thereafter, rising to a 8.5 percent move hazard by rate gap of 1 and flattening again thereafter.
Figure 4: Moves Within and Across Labor Market Areas versus Rate Gap
Note: The left panel plots estimated probability of moves
either within the same CBSA (blue) or to a different CBSA (red) using
coefficient estimates from regression equation (2)
evaluated at the mean of controls using the imputed market rate to
create rate gaps. The right panel similarly plots estimated probability
of moves under 30 miles from the origin zip code (blue) to the
probability of moving more than 50 miles away (red). Moves between 30-50
miles are excluded, but are quite uncommon. Dashed bars are 95 percent
confidence bands. Changes in rate gaps appear to affect same-CBSA moves
(left panel) and moves within 30 miles of the origin zip code (right
panel), each of which are likely more discretionary. Moves across CBSAs
or more than 50 miles away–which are more likely to reflect changes in
jobs–are almost unaffected by rate gaps, suggesting effects on labor
allocations may be minimal.
Source: Authors’ calculations
using ICE/McDash and Equifax CRISM, Cotality Deeds, Freddie Mac
PMMS
The left panel of Figure 4 plots estimated probability of moves either within the same CBSA (blue) or to a different CBSA (red) using coefficient estimates from regression equation (2) evaluated at the mean of controls using the imputed market rate to create rate gaps. The right panel displays the same for moves within 30 miles and over 50 miles. (Moves between 30-50 miles are excluded, but are a small share of moves. Including moves up to 50 miles yields a definition highly correlated with within-CBSA.) Dashed bars are 95 percent confidence bands. Broadly, the picture shows moves across CBSAs or more than 50 miles away–which are more likely to reflect changes in jobs–are almost unaffected by rate gaps, suggesting effects on labor allocations may be minimal. Rate gaps appear to instead affect shorter distance moves within CBSAs.
In the left panel, red confidence bands reflecting cross-CBSA moves appear much lower than blue confidence bands for within-CBSA moves, reflecting the fact that 2/3 of moves are within CBSA. The likelihood of within-CBSA moves rises from 2.5 percent annualized hazard at rate gaps of -3 and -2, to about a 3 percent annualized hazard at 1 percent rate gaps, before flattening. The likelihood of cross-CBSA moves remains close to 2 percent, rising only modestly over the rate gap range displayed.
Similarly in the right panel, red confidence bands for moves over 50 miles appear much lower than blue confidence bands for moves under 30 miles, reflecting the fact short-distance moves are more common. The likelihood of under-30-mile moves rise from 2.25 percent annualized hazard at low rate gaps (-3 to -2) to 2.75 percent at high rate gaps of +1.25 percent before flattening at this level. The likelihood of over-50-mile moves remains low, rising only modestly between 1.75 percent and 2 percent over this same interval.
Figure 5: Effect of Lock-In on CBSA-level Active Listings
Note: This figure plots coefficients from two-way fixed effect
difference-in-difference style regression. The figure shows the effect
of a one standard deviation increase in CBSA exposure to lock-in (as
measured by CBSA average ’missing move’ rate) on the natural log of
monthly active real estate listings in that CBSA, according to
Realtor.com data. The omitted month is December 2021, just prior to the
Federal Reserve’s rate hikes. The coefficients can be interpreted as a
percentage change from that baseline. Regressions include CBSA and
month-year fixed effects. Standard errors are clustered at the CBSA
level.
Source: Authors’ calculations using ICE/McDash and
Equifax CRISM, PMMS, Realtor.com MLS data
Figure 5 plots coefficients from two-way fixed effect difference-in-difference style regression. The figure shows the effect of a one standard deviation increase in CBSA exposure to lock-in (as measured by CBSA average `missing move' rate) on the natural log of monthly active real estate listings in that CBSA, according to Realtor.com data. The omitted month is December 2021, just prior to the Federal Reserve's rate hikes. The coefficients can be interpreted as a percentage change from that baseline, and standard error bands are shown in vertical bars. Estimates hover near zero and are insignificant in all quarters through February 2022, after which estimates fall to show a 10 percent decline in listings by mid-2022 where they remain through March 2024 where data end. Regressions include CBSA and month-year fixed effects. Standard errors are clustered at the CBSA level.
Figure 6: Effect of Exposure to Lock-In on CBSA House Price Growth
(a) All CBSAs
(b) Largest 300 CBSAs
Note: This figure plots coefficients from a two-way fixed effect
difference-in-difference style specification, which regresses log
year-over-year house price changes at the CBSA level (according to
Cotality’s CBSA House Price Index) on CBSA-level exposure to lock-in.
Effect sizes are for a one standard deviation increase in CBSA exposure
to lock-in (as measured by CBSA average ’missing move’ rate). The left
panel includes all 699 CBSAs (out of 767 in our data) for which we have
at least 500 loans in the Dec. 2021 cross-section of the Equifax CRISM
dataset. The right panel is restricted to the 300 largest CBSAs in our
sample. Magnitudes are greater when limiting the sample to the largest
metro areas. For both panels, the omitted month is December 2021, just
prior to the Federal Reserve’s rate hikes. The coefficients can be
interpreted as a percentage change from that baseline. Regressions
include CBSA and month-year fixed effects. Standard errors are clustered
at the CBSA level.
Source: Authors’ calculations using
ICE/McDash and Equifax CRISM, Cotality Deeds, Freddie Mac PMMS, Cotality
HPI
Figure 6 plots coefficients from a two-way fixed effect difference-in-difference style specification, which regresses log year-over-year house price changes at the CBSA level (according to CoreLogic's CBSA House Price Index) on CBSA-level exposure to lock-in. Effect sizes are for a one standard deviation increase in CBSA exposure to lock-in (as measured by CBSA average `missing move' rate). For both panels, the omitted month is December 2021, just prior to the Federal Reserve's rate hikes. The coefficients can be interpreted as a percentage change from that baseline. Regressions include CBSA and month-year fixed effects. Standard errors are clustered at the CBSA level. The left panel includes all 699 CBSAs (out of 767 in our data) for which we have at least 500 loans in the Dec. 2021 cross-section of the Equifax CRISM dataset. Estimates remain near zero prior to mid-2022 when they rise to show a 2 percent increase in price growth by the end of 2022, where they remain through the end of the sample in March 2024. The right panel is restricted to the 300 largest CBSAs in our sample. Magnitudes are greater when limiting the sample to the largest metro areas, rising to a 3 percent effect on annual price growth by the end of 2022 and receding slightly to a 2 percent contribution to growth at the end of our sample in March 2024.
Figure 7: Effect of Lock-In Exposure on Housing Market Tightness
(a) Log Median Days on Market
(b) List Prices, YoY
change
(c) Log Price Reductions
Note: This figure
plots coefficients from a two-way fixed effect difference-in-difference
style specification, which regresses various CBSA-level measures of
market tightness on CBSA-level exposure to lock-in. The top panel (a)
plots the effect of lock-in on the log of the 12-month moving average of
median days on market (according to Realtor.com data). The bottom left
panel (b) shows the 12 month change in list prices and the bottom right
panel (c) shows the share of listings with price reductions. For all
panels, the omitted month is December 2021, just prior to the Federal
Reserve’s rate hikes. The coefficients can be interpreted as a
percentage change from that baseline. Regressions include CBSA and
month-year fixed effects. Standard errors are clustered at the CBSA
level.
Source: Authors’ calculations using ICE/McDash and
Equifax CRISM, Cotality Deeds, Freddie Mac PMMS,
Realtor.com
Figure 7 plots coefficients from a two-way fixed effect difference-in-difference style specification, which regresses various CBSA-level measures of market tightness on CBSA-level exposure to lock-in. The top panel (a) plots the effect of lock-in on the log of the 12-month moving average of median days on market (according to Realtor.com data). The bottom left panel (b) shows the 12 month change in list prices and the bottom right panel (c) shows the log count of listings with price reductions. For all panels, the omitted month is December 2021, just prior to the Federal Reserve's rate hikes. The coefficients can be interpreted as a percentage change from that baseline. Regressions include CBSA and month-year fixed effects. Standard errors are clustered at the CBSA level.
In panel (a), estimates of the effect of lock-in on log median days on market hovers near zero through 2021. After rate hikes, days on market fall gradually to 4 percent below their initial level by Spring 2023 where they remain through the end of the data in March 2024. In panel (b), annual changes in listing prices hover near zero through 2021 after which growth in listing prices rises by about 3 percent in late 2022. Estimates are noisy, but remain within the range of price growth estimates shown previously. In panel (c), log price reductions on listings hover near zero through the full sample period suggesting selling prices remain near listing prices in similar fashion both before and after rate hikes.
Figure 8: Lock-in Driven House Price Growth is Stronger in Tighter
Markets
(a) Log Days on Market (Tight Markets)
(b)
Log Days on Market (Loose Markets)
(c) Log House Price Growth
(Tight Markets)
(d) Log House Price Growth (Loose Markets)
Note: This figure plots coefficients from a two-way fixed
effect difference-in-difference style specification. Panels (a) and (b)
regress log of the 12-month moving average of median days on market
(according to Realtor.com) on CBSA-level exposure to lock-in. Panels (c)
and (d) do the same for the log year-over-year house price changes at
the CBSA level (according to Cotality’s CBSA House Price Index). In both
cases, the set of left panels (Panels (a) and (c)) include CBSAs with
above median market-tightness prior to the rate hikes where market
tightness is measured as the CBSAs median days on market in 2021Q4. The
right panels (Panels (b) and (d)) include CBSAs with below median
market-tightness. Magnitudes of both time on market effects and price
effects are two to three times larger for markets above median
tightness. In all panels, the omitted month is December 2021, just prior
to the Federal Reserve’s rate hikes. The coefficients can be interpreted
as a percentage change from that baseline. Regressions include CBSA and
month-year fixed effects. Standard errors are clustered at the CBSA
level.
Source: Authors’ calculations using ICE/McDash and
Equifax CRISM, Cotality Deeds, Freddie Mac PMMS, Cotality HPI,
Realtor.com
Markets
Figure 8 plots coefficients from a two-way fixed effect difference-in-difference style specification. Panels (a) and (b) regress log of the 12-month moving average of median days on market (according to Realtor.com) on CBSA-level exposure to lock-in. Panels (c) and (d) do the same for the log year-over-year house price changes at the CBSA level (according to CoreLogic’s CBSA House Price Index). In both cases, the set of left panels (Panels (a) and (c)) include CBSAs with above median market-tightness prior to the rate hikes where market tightness is measured as the CBSAs median days on market in 2021Q4. The right panels (Panels (b) and (d)) include CBSAs with below median market-tightness. Magnitudes of both time on market effects and price effects are two to three times larger for markets above median tightness. In all panels, the omitted month is December 2021, just prior to the Federal Reserve’s rate hikes. The coefficients can be interpreted as a percentage change from that baseline. Regressions include CBSA and month-year fixed effects. Standard errors are clustered at the CBSA level.
In panel (a), estimated effects on log days on market in initially tight markets hovers near zero through 2021 before falling to about 5 percent below baseline levels by the end of 2022, where they remain through the end of the data in March 2024. In panel (b), effects on log days on market in initially loose markets shows a similar, though more muted pattern, falling only 2-3 percent from baseline. In panel (c), price growth in tight markets hovers near zero through 2021 and then displays a roughly 3 percent contribution from lock-in by the end of 2022 before receding slightly to 2 percent at the end of our data period in March 2024. In panel (d), price growth in loose markets displays a similar, though more muted pattern, rising only o 1.75 percent over this period.
Figure 9: Stylized Local Estimate to Large Lock-in Shock with Convex
Aggregate Response
Note: This figure shows a stylized
depiction of our baseline estimate. In the example, two cities begin at
the same initial tightness and experience a large shock to churn, though
one city has a larger shock (blue) and the other has a smaller shock
(orange). Cross-CBSA estimates reduce to a simple
difference-in-difference in the two-city case, and recover a LATE for
the slope shown in purple reflecting variation in the circled range. Due
to convexity in the aggregate effect (i.e., marginal effects rising in
tightness), the LATE recovered by the DiD (purple) is much steeper than
the average slope over the range of the shock.
Aggregate Response
Figure 9 shows a stylized depiction of our baseline estimate. The figure displays a convex (rising slope) relationship between the lock-in shock (x-axis) and the effect on tightness (y-axis) as described by Equation (8) from the model. In the example, two cities begin at the same initial tightness (displayed as a point on the x-axis) and experience a large shock to churn (displayed as a shift right on the axis). One city experiences a larger shock (blue) and the other has a smaller shock (orange), leading the first city (blue) to land to the right of the second city (orange). Cross-CBSA estimates reduce to a simple difference-in-difference in the two-city case, and recover a LATE for the slope shown in purple reflecting variation in the circled range around the convex relationship between the end points of the two cities. Due to convexity in the aggregate effect (ie, marginal effectsrising in tightness), the LATE recovered by the DiD (line with slope equal to average in the region between high/low shock cities, drawn in purple) is much steeper than the average slope over the range of the shock.
Figure 10: Lock-in Price Effects Heat Map
Note: This figure
shows a heat map of aggregate price effects from different sized shocks
to mobility (y-axis) and different initial levels of time on market (our
proxy for tightness, x-axis). Red shading reflects larger positive price
effects and blue shading reflects larger negative price effects. Effects
rise exponentially in the size of the shock and depend on initial market
tightness, with a balanced-market point of 64.4 days on market showing
null as tightness is not changed by equal declines in buyers and
sellers. The black contour shows the middle 50% density
(population-weighted) of shock sizes and time on market in December
2021, with the shading within the region representing effects seen by
cities within this range. The pink region represents a counterfactual
shock of half the magnitude, with smaller effects. The orange region
represents a counterfactual shock of a similar size as 2022 rate hikes
but under market conditions in 2019. Effects on prices would have been
muted in such a scenario despite a large drop in listings and moves, as
the market was more balanced between buyers and sellers.
Source: Authors’ calculations using ICE/McDash and Equifax CRISM,
Cotality deeds and house prices, Realtor.com data
Figure 10 shows a heat map of the aggregate price effects from different sized shocks to mobility (y-axis) and different initial time on market (x-axis). The horizontal line representing a zero shock is unshaded/white, with exponentially increasing shading representing increasingly large effects as the shock size rises. The rate of this increase or decrease is determined by the position on the x-axis representing initial market tightness, with no effects along the “balanced market” point at roughly 64.4 days on market, increasingly large positive price effects (redder shading) for tighter initial conditions (lower days on market), and increasingly large negative price effects (blue shading) for slacker initial conditions (higher days on market). A black contour represents the distribution of actual shock sizes and initial conditions across CBSAs in December 2021, drawn to the bottom left where effects are largest. A shock of half the size is represented by the pink contour, showing effects that are smaller than half as large due to the convexity in effect sizes along the y-axis. A shock of similar size under initial tightness conditions from 2019 shows most cities would experience small effects (with some positive and some negative) as represented by the contour drawn at roughly 63 days on market, just tighter than the balanced market point.
Figure 11: Lock-in Spillovers to Rental and Construction Markets
(a) Rents (y/y growth)
(b) Single Family Permits
(prior 12 months)
Note: This figure plots the effects of
lock-in on rents (left panel) and single-family permits (right panel).
Year-over-year growth in rents rose about 2 to 3 percentage points
higher in areas with higher lock-in exposure as tighter owner-occupied
housing markets disincentivized renters from transitioning to ownership.
Rents figure shows 12 month changes in Zillow rental index for all
rental units 467 CBSAs accounting for 93 percent of loans observed.
Permits figure uses total single family permits from prior 12 months
from Census’s Building Permits Survey for 712 CBSAs accounting for 99
percent of loans observed. Permits series are normalized by historic
annual permitting rate for CBSA to account for differences in permitting
rates across places.
Source: Authors’ calculations using
ICE/McDash and Equifax CRISM, Cotality Deeds, Freddie Mac PMMS, Zillow
Rents, Census Building Permit Survey
Figure 11 plots the effects of lock-in on rents (left panel) and single-family permits (right panel). Point estimates in the left panel show year-over-year growth in rents in more exposed metro areas rose to 2 to 3 percentage points higher than in less exposed areas by the end of 2022 before leveling out. Similarly, permitting appears to rise 20 percent in 2022 in response to lower inventories in more lock-in exposed areas before receding somewhat to statistically insignificant levels by 2023.
Figure A.1: Alternate specifications – Mobility by Rate Gap, 2009-2023
Note: This figure plots estimated monthly probability of
moving conditional on borrower’s rate gap using estimated regression
coefficients from Equation (2) evaluated at mean of control variables.
The plot shows several alternate specifications compared to our baseline
specification in black. Red dots are estimated using rate gaps defined
as the difference between actual origination rates and imputed market
rates at the time of observation, and do not include controls for
forgone refinancing opportunities. Blue dots also exclude controls for
forgone refinancing opportunities, but utilizes rate gaps constructed
using imputed borrower rates at both origination and current time. Green
dots repeat our baseline specification using rate gaps constructed using
PMMS prime rates for all borrowers at both origination time and current
time. In all specifications, dashed lines mark 95 percent confidence
bands using standard errors clustered at origination cohort.
Source: Authors’ calculations using ICE/McDash and Equifax CRISM,
Cotality deeds data, Freddie Mac
Figure A.1 plots estimated monthly probability of moving conditional on borrower's rate gap using estimated regression coefficients from Equation (2) evaluated at mean of control variables. The plot shows several alternate specifications compared to our baseline specification in black. Red dots are estimated using rate gaps defined as the difference between actual origination rates and imputed market rates at the time of observation, and do not include controls for forgone refinancing opportunities. Blue dots also exclude controls for forgone refinancing opportunities, but utilizes rate gaps constructed using imputed borrower rates at both origination and current time. Green dots repeat our baseline specification using rate gaps constructed using PMMS prime rates for all borrowers at both origination time and current time. In all specifications, dashed lines mark 95 percent confidence bands using standard errors clustered at origination cohort.
We direct the reader to the text of Appendix A which walks through each specification in detail, but describe them briefly here. Beginning with the simplest specification with no maximum rate gap control and utilizing actual origination rates (red), the shape appears relatively flat rising from a predicted move probability of 6 percent to just under 7 percent as rate gaps go from -3 to 0. Move probabilities rise slightly to 8 percent by a rate gap of 1 at which point they flatten once again. Moving to a version no maximum rate gap control, but utilizing imputed origination rates, the slope steepens. At a rate gap of -3, the predicted move probability is 3 percent, rising to about 6 percent for a rate gap of 0. The hazard continues to rise to a little above 8 percent by rate gap of 1 and nearly 10 percent at rate gap of 2. Moving to the black specification—our baseline—the slope flattens again, appearing quite similar to the initial specification in red dots. This is described in the main text figure. The green dots reflect a version that repeats our baseline specification using PMMS market rates at origination and current periods in lieu of our imputation and yields very similar results to the black dots, aside from being slightly steeper in the tails.
Figure A.2: Alternate Max Rate Gap Controls – Mobility by Rate Gap,
2009-2023
Note: This figure plots estimated monthly
probability of moving conditional on borrower’s rate gap using estimated
regression coefficients from Equation (2) evaluated at mean of control
variables. The plot shows several alternate definitions of our maximum
rate gap control compared to our baseline specification in black. Blue
dots show estimates that exclude any control for rate gap history (same
as blue dots in A.1). Green, black,
and red dots include a control for the maximum rate gap observed between
origination and 1 month ago, 3 months ago (baseline), and 6 months ago,
respectively.
Source: Authors’ calculations using ICE/McDash
and Equifax CRISM, Cotality deeds data
2009-2023
Figure A.2 plots estimated monthly probability of moving conditional on borrower’s rate gap using estimated regression coefficients from Equation (2) evaluated at mean of control variables. The plot shows several alternate definitions of our maximum rate gap control compared to our baseline specification in black. Blue dots show estimates that exclude any control for rate gap history (same as blue dots in A.1). Green, black, and red dots include a control for the maximum rate gap observed between origination and 1 month ago, 3 months ago (baseline), and 6 months ago, respectively.
We refer the reader to Figure A.1 for descriptions of the black and blue dots which correspond exactly to the similar lines on that chart. Red dots include the maximum rate gap through 6 months prior as a control, and results appear somewhat flatter than the blue dots with no maximum rate gap control. Black dots—our baseline—include the maximum rate gap through 3 months prior as a control, and flatten the slope further. Green dots include the maximum rate gap through only one month prior as a control, and results appear to flatten even further than the black dots, albeit only slightly.
Figure B.3: Heterogeneity in Move Distances and Reasons
Note:
This figure shows heterogeneity in move distances and reasons. The top
panel shows the distribution of move distances among households with
mortgages. About 1/2 of moves are within 30 miles and 2/3 are within the
same CBSA, likely providing access to the same jobs as before. Few moves
occur to intermediate distances, and most longer distance moves are to
different CBSAs. The bottom panel shows most job-related moves tend to
be across state lines, while most short-distance moves are motivated by
desire to improving housing or neighborhood quality/size or due to
family structure reasons.
Sources: Top panel – FRBNY/Equifax
Consumer Credit Panel, sample of borrowers with mortgage prior to move.
Bottom panel – CPS (ASEC), 2010-2023, sample of homeowners with
mortgage
Figure B.3 shows heterogeneity in move distances and reasons among households with mortgages. The top panel shows a bar chart of the distribution of move distances among households with mortgages in the CCP data, splitting also between within-CBSA (blue) and cross-CBSA moves (red). About 1/2 of moves are within 30 miles and 2/3 are within the same CBSA, likely providing access to the same jobs as before. Few moves occur to intermediate distances (100-300 miles), and most moves over 50 miles are to different CBSAs. The bottom panel shows the distribution of moves among mortgagors split into reasons for the move (Family, House/Neighborhood, New job, Other work-related, Other) in the CPS ASEC. Bars for each reason are split into same county (blue), different county/same state (gray) and different state (red). Most moves are for either family (29.2%) or housing/neighborhood reasons (51.7%), with a smaller share related to new jobs (8.9%) or other work-related motives (3.6%). The remainder of moves (6.5%) are for other reasons. Most moves for family and housing/neighborhood reasons are within the same county or the same state (combined, 89.0% of family and housing/neighborhood moves are within state and 68.5% are within the same county). Most job-related moves tend to be across state lines (about 62.1% of job-related moves are out of state).
Figure B.4: Time Series of Moves by Type
Note: This figure
shows moves (zip code changes) in credit records for borrowers that had
a mortgage prior to the move. The figure splits moves into those within
CBSA (blue) and across CBSAs (red), and shows aggregate declines in
moves over 2022 were driven primarily by short-distance (within CBSA)
moves, which tend to be driven by consumption-related reasons.
Across-CBSA moves remained relatively flat.
Source:
FRBNY/Equifax Consumer Credit Panel
Figure B.4 shows moves (zip code changes) in credit records for borrowers that had a mortgage prior to the move. The figure splits moves into those within CBSA (blue) and across CBSAs (red). Mobility increased somewhat between 2010 and 2022 in the credit bureau data, led primarily by an increase in the cross-CBSA moves. The figure shows aggregate declines in moves over 2022-2023 were driven primarily by short-distance (within CBSA) moves, which tend to be driven by consumption-related reasons. Across-CBSA moves remained relatively flat in 2022 and dipped down modestly in 2023.
Figure F.1: Rate gap change vs predicted rate gap
Note: This
figure plots the relationship between the rate gap we predict and the
true change in rate among movers who both sell their home and
subsequently purchase a new home using a mortgage. The graph is a
binscatter with 100 bins, estimated on the subset of movers whose
subsequent mortgage we could identify in the CRISM
database.
Figure F.1 Rate Gap Change vs Predicted Rate Gap.
Figure F.1 plots the relationship between the rate gap we predict and the true change in rate among movers who both sell their home and subsequently purchase a new home using a mortgage. The graph is a binscatter with 100 bins, estimated on the subset of movers whose subsequent mortgage we could identify in the CRISM database. The binscatter (blue dots) falls very close to the 45 degree line drawn in red, suggesting a strong alignment between our rate gap measure and realized rate gaps for the subset of loans on which we can observe the subsequent mortgage rate.
Figure F.2: Simulating Missing Moves Due to Rate Changes
Note: This figure plots actual moves in each calendar year (gray bars).
Red bars represent the additional moves (or fewer moves, if negative)
expected if the rate gap distribution had not changed from the prior
year, computed as described in 3.3.
Taking 2022 as an example, the gray bar shows that about 2.9 million
moves occurred, down from over 4.5 million in 2021. The red bar in 2022
suggests around 719 thousand more moves would have occurred in 2022 had
borrowers faced the same rate gap as they did before the 2022’s mortgage
rate hikes. In other words, about 44% of the drop in moves between 2021
and 2022 can be explained by shifts in the rate gap distribution and the
resulting lock-in effect.
Source: Authors’ calculations using
ICE/McDash and Equifax CRISM, Cotality deeds data, Freddie Mac
PMMS
Figure F.2 plots actual moves in each calendar year (gray bars). Changes in the rate gap distribution from year to year, coupled with the non-linear relationship between rate gaps and moves in Figure 3 provides an estimate of the contribution of rate changes to the year-to-year change in mobility (shown in red bars). Moves begin at about 2 million in 2009-2010, rising gradually to around 4 million by 2019. Contributions from rate gap changes are small in these earlier years as rate fluctuations are modest. In 2020-2021, actual moves rise to around 4.5 million, and red bars contribute negatively, suggesting without rate declines during this period, moves would have been lower. Moves fall sharply to 3 million in 2022 and 2 million in 2023 as rates rise. Red bars show about 44% of the decline in moves from 2021 to 2022 can be explained by the sharp increase in rates over the period, and a similar fraction in 2023.
Figure F.3: Move rates by tenure at destination
Note: This
figure plots mobility rates in the FRBNY/Equifax Consumer Credit Panel
for borrowers with mortgages split by distance of destination and tenure
at destination. The left panel shows annualized likelihood of moving
within a CBSA split into moves where the borrower had a mortgage at the
destination location (own-to-own moves) in blue and moves where the
borrower did not have a mortgage at the destination (likely own-to-rent
moves) in red. The decline in within-metro churn appears to be driven by
own-to-own moves falling, while own-to-rent moves remain rather flat.
The right panel repeats the same for moves out of a CBSA, with the small
drop in cross-CBSA moves again driven by a decline in own-to-own
moves.
Figure F.3 plots mobility rates in the FRBNY/Equifax Consumer Credit Panel for borrowers with mortgages split by distance of destination and tenure at destination. The left panel shows annualized likelihood of moving within a CBSA over time split into moves where the borrower had a mortgage at the destination location (own-to-own moves) in blue and moves where the borrower did not have a mortgage at the destination (likely own-to-rent moves) in red. Both lines appear relatively flat prior to 2022, with own-to-own moves showing a higher level (1.5 percent versus 0.5 percent). The 2022 decline in within-metro churn appears to be driven by own-to-own moves falling to 1 percent by 2024, while own-to-rent moves remain rather flat. The right panel repeats the same for moves out of a CBSA, with the small drop in cross-CBSA moves in 2022 again driven by a decline in own-to-own moves.
Figure F.4: Effect of Rate Gap and Home Value Change for Within- and
Between-CBSA Movers
Note: This figure plots the estimated log
change in house value between movers’ old and new houses. As in Figure
3, value changes are created
using coefficients from Equation (2) evaluated at the mean of control
variables. The rate gap is defined as the difference between the rate
available to the borrower at origination and the imputed market rate for
the borrower, with all the same controls as in Figure 3. Dashed lines mark 95
percent confidence bands.
Source: Authors’ calculations using
ICE/McDash and Equifax CRISM, PMMS, and Cotality Deeds
Between-CBSA Movers
Figure F.4 plots the estimated log change in house value between movers’ old and new houses. As in Figure 3, value changes are created using coefficients from Equation 2 evaluated at the mean of control variables. The rate gap is defined as the difference between the rate available to the borrower at origination and the imputed market rate for the borrower, with the same controls as included in Figure 4. Dashed lines mark 95 percent confidence bands. Blue dots reflect same CBSA moves, which show a slightly higher change in home values across moves than cross-CBSA moves shown in red. In both cases, the point estimates show a modest positive slope, although results are noisy and do not show statistically significant differences across the range of rate gaps shown.
Figure F.5: Geographic Variation in Exposure to Lock-In
Note:
This figure plots estimated CBSA-level exposure to the Lock-In shock, as
described in Section 4.
Exposure is measured in standard deviations from the mean CBSA. The top
map includes all metros in our sample; the bottom includes just the 300
most populated CBSAs. Exposure thresholds are consistent across the two
maps.
Source: Authors’ calculations using ICE/McDash and
Equifax CRISM, PMMS, and Cotality Deeds
Figure F.5 shows a map of estimated CBSA-level exposure to the Lock-in shock, as described in Section 4.2. Exposure is measured in standard deviations from the mean across CBSAs. The top map includes all metros in our sample and the bottom shows only the top 300 most populous CBSAs. Lock-in exposure appears somewhat higher in smaller locations and locations in the Midwest and northeast, although there is considerable heterogeneity even within states and regions.
Figure F.6: Housing Inventory: Days on Market and Active Listings
Note: This figure plots the 12 month moving averages of days
on market and active listings across the US. Days on market are defined
as the 12 month average over the median across listings in each month.
Active listings are the 12 month average listing count. Both metrics
fall sharply over COVID, suggesting market tightening as buyer demand
exceeded sellers on the market, and partially renormalize starting in
2022. Results suggest renormalization was slowed by lock-in.
Source: Realtor.com
Figure F.6 plots the 12 month moving averages of days on market and active listings across the US. Days on market are defined as the 12 month average of median time on market for listings in each month. Active listings are the 12 month average listing count. Days on market hovers near 65 days with about 1.2 million listings between 2018 and 2020, before both series fall sharply during COVID. Days on market reaches a trough of 40 days at the end of 2021, with active listings falling to below 500 thousand in the fourth quarter of 2021. These moves suggest the market tightened as buyer demand exceeded sellers on the market before partially renormalize starting in 2022. Days on market rises (with some fluctuations) to just below 60 days at the end of 2024, as listings rise to about 900 thousand over this same period. Results from the paper suggest this renormalization was held back by lock-in.
Figure F.7: Lock-in Effects on House Price Growth w/ Additional Controls
House Price Growth: Baseline \(+\) Init. Mkt. Tightness \(\times\) Time
House Price
Growth: Baseline \(+\) High DTI Share
\(\times\) Time
House Price
Growth: Baseline \(+\) Subprime Share
\(\times\) Time
Market
Tightness (Log Days on Market): Baseline \(+\) Init. Mkt. Tightness \(\times\) Time
Market Tightness
(Log Days on Market): Baseline \(+\)
High DTI Share \(\times\) Time
Market Tightness (Log Days on Market): Baseline \(+\) Subprime Share \(\times\) Time
Figure F.7 plots event study estimates
similar to our baseline, but with additional controls. The top panels
plots coefficients from a two-way fixed effect difference-in-difference
style specification which regresses house price growth at the CBSA level
(from Cotality House Price Index) on CBSA-level exposure to lock-in. The
bottom panels do the same for market tightness, proxied by log days on
market (from Realtor.com). In addition to month and CBSA fixed effects,
the left panels of each row include initial market tightness (log days
on market in 2021q4) interacted with time to address potential concerns
that estimated lock-in effects are attributable to trends in initial
market tightness. As shown in Appendix Figure F.6, the trend in market
tightness actually suggested a slackening over this period, and
estimated effects of lock-in remain similar to our baseline. Similarly,
the middle and right panels include the share of high-DTI originations
(DTI above 50 percent) and share of subprime borrowers interacted with
time to address concerns that lock-in effects may be picking up direct
effects of rates may pass through differently to higher risk borrowers.
While this channel may be present, it appears largely orthogonal to
lock-in, and impacts of lock-in remain similar to the baseline estimate.
Source: Authors’ calculations using ICE/McDash and Equifax
CRISM, Cotality Deeds, Cotality HPI, Realtor.com.
Figure F.7 plots event study estimates similar to our baseline, but with additional controls. The top panels plots coefficients from a two-way fixed effect difference-in-difference style specification which regresses house price growth at the CBSA level (from Corelogic House Price Index) on CBSA-level exposure to lock-in. The bottom panels do the same for market tightness, proxied by log days on market (from Realtor.com). In addition to month and CBSA fixed effects, the left panels of each row include initial market tightness (log days on market in 2021q4) interacted with time to address potential concerns that estimated lock-in effects are attributable to trends in initial market tightness. Similarly, the middle and right panels include the share of high-DTI originations (DTI above 50 percent) and share of subprime borrowers interacted with time to address concerns that lock-in effects may be picking up direct effects of rates may pass through differently to higher risk borrowers. For prices (top panels), each column shows a similar pattern to the baseline specification with point estimates near zero and confidence bands covering zero prior to 2021. Beginning in the middle of 2022, we see point estimates rise gradually to a peak of 0.02 in mid-2022 before decreasing to 0.01at the end of the sample in March 2024. Effects in the right most panel (including subprime share interactions) are slightly smaller, with a peak of just below 0.02, but confidence bands cover the same region as in other estimates. For tightness (bottom panels), point estimates are a little above zero in 2018 but hover near zero through 2021, with confidence bands covering zero for all months between 2019 and 2021. Results show point estimates gradually fall to -.04 in the left and middle panels by the end of 2022 and remain stable at this point. Results in the right most panel (including subprime share interactions) are somewhat more muted, falling to -.03.
Figure F.8: Effect of Exposure to Lock-In on CBSA House Price Growth
Overall vs. Top300 CBSAs vs. All Others
Note: This
figure plots coefficients from a two-way fixed effect
difference-in-difference style specification, which regresses log
year-over-year house price changes at the CBSA level (according to
Cotality’s CBSA House Price Index) on CBSA-level exposure to lock-in.
Effect sizes are for a one standard deviation increase in CBSA exposure
to lock-in (as measured by CBSA average ’missing move’ rate). The left
panel includes all 699 CBSAs (out of 767 in our data) for which we have
at least 500 loans in the Dec. 2021 cross-section of the Equifax CRISM
dataset. The middle panel is restricted to the 300 largest CBSAs in our
sample; the right panel contains all CBSAs not in the top 300. The
coefficients can be interpreted as a percentage change from that
baseline. Regressions include CBSA and month-year fixed effects.
Standard errors are clustered at the CBSA level.
Source:
Authors’ calculations using ICE/McDash and Equifax CRISM, Cotality
Deeds, Freddie Mac PMMS, Cotality HPI
Overall vs. Top300 CBSAs vs. All Others
Figure F.8 plots coefficients from a two-way fixed effect difference-in-difference style specification, which regresses log year-over-year house price changes at the CBSA level (according to CoreLogic's CBSA House Price Index) on CBSA-level exposure to lock-in. Effect sizes are for a one standard deviation increase in CBSA exposure to lock-in (as measured by CBSA average `missing move' rate). The left panel includes all 699 CBSAs (out of 767 in our data) for which we have at least 500 loans in the Dec. 2021 cross-section of the Equifax CRISM dataset. The middle panel is restricted to the 300 largest CBSAs in our sample; the right panel contains all CBSAs not in the top 300. The coefficients can be interpreted as a percentage change from that baseline. Regressions include CBSA and month-year fixed effects. Standard errors are clustered at the CBSA level.