Positive Rental History

By Wenzhen Lin and Jeffrey Perry, Housing Finance Analysis Division, Policy Development & Research

Summary

Positive rental history (PRH) has been included as an element of the Federal Housing Administration's (FHA's) Technology Open to Approved Lenders (TOTAL) Scorecard since October 30, 2022, and lenders have been required to report PRH since March 25, 2023. FHA incorporated PRH in TOTAL to expand access to credit for first-time homebuyers who have demonstrated a history of making on-time rent payments, which generally are not reflected in credit scores. Because of this policy change, as of August 31, 2023, 1,727 endorsements that otherwise would have required manual underwriting were accepted through TOTAL. On average, these borrowers were slightly younger, were more likely to be first-time homebuyers, had lower credit scores, and had less wealth. They also were more likely to be black or female. Although there has been limited time to observe loan performance, PRH was not found to have a statistically significant correlation with default.

Background

Borrowers are considered to have PRH when they can demonstrate a history of on-time rent payments of at least $300 per month for the previous 12 months. To be eligible, at least one borrower must have PRH, and the loan must also meet the following requirements:

• - The transaction is a purchase.
• - At least one borrower is a first-time homebuyer.
• - The minimum decision credit score is at least 620.

Fannie Mae has included PRH in its underwriting decisions since September 2021, and Freddie Mac has included it since July 2022.

FHA incorporates PRH into the TOTAL decision by reducing the threshold for acceptance for applicants with PRH. This approach is supported by research concluding that including rent payment history in credit scoring calculations increases the credit score of those who make on-time payments. Turner and Walker (2019) found that, among tenants of public housing agencies, including positive rental payment data increased credit scores for approximately 90 percent of tenants, and for approximately 50 percent of tenants, credit scores increased by more than 20 points. Another report found that including the positive rental payments of affordable housing tenants in credit scoring increased their credit scores by an average of 23 points.

PRH Acceptance Trends and Patterns

TOTAL began to accept mortgages that qualified through PRH after October 30, 2022, but PRH endorsements were very low until March 25, 2023, when lenders were required to report it. Through August 31, 2023, 1,727 endorsements had received an accept decision from TOTAL because of PRH. The number of endorsements that qualified through PRH, between the PRH threshold X-β and the standard threshold X, remains lower than the count of PRH endorsements that were already above the threshold (figure 1). Those endorsements constituted approximately 0.4 percent of purchase loans since October 2022. The share of endorsements that qualified through PRH was approximately 0.2 percent in March 2023 and rose to 0.7 percent in August 2023 (figure 2). If the share of mortgages that qualify through PRH remains steady at approximately 0.7 percent of purchase endorsements, PRH endorsements would make up approximately 5,600 mortgages in a typical year of 800,000 purchase mortgage originations, or approximately $1.2 billion on a volume of $175 billion.

Figure 1: Counts of Purchase Endorsements With Positive Rental History Near the Threshold

Source: SFHEDW, mortgages with a positive rental history endorsed on or after October 30, 2022.
Note: X represents the threshold for an accept decision under TOTAL, and X–β represents the new threshold under PRH. The bars represent the number of mortgages that have a positive rental history within each TOTAL score increment.

Figure 2: Share of Purchase Endorsements Accepted Through Positive Rental History

Source: SFHEDW, mortgages endorsed on or after October 30, 2022.

Borrower and Loan Characteristics Analysis

Table 1 summarizes borrower and loan characteristics for home purchase mortgage endorsements from on or after October 30, 2022. Column 1 presents the summary statistics for purchase loans that were manually underwritten, and columns 2 through 5 show purchase loans accepted through TOTAL. The data show that borrowers who were accepted because of PRH (column 2, with scores between the PRH threshold and the usual threshold) have different characteristics than the average for all borrowers accepted through TOTAL (column 5). PRH borrowers tend to be slightly younger compared with borrowers in other categories. In addition, a higher proportion of these borrowers are first-time homebuyers, have higher loan-to-value ratios, and have higher back-end debt-to-income ratios. Furthermore, these borrowers hold significantly fewer assets, have smaller mortgage amounts, and have lower incomes.

Borrowers who were accepted because of PRH (column 2) have characteristics that are similar to borrowers who were just above the threshold (column 3). These two groups have similar FICO scores, loan-to-value ratios, back-end ratios, annual incomes, and mortgage amounts. However, borrowers who were accepted because of PRH have fewer assets than do borrowers who were just above the threshold.

Table 1: Summary Statistics of Purchase Endorsements by TOTAL Score Category

Characteristics	Manually Underwritten	PRH  [X–β, X)	[X, X+β)	[X+β, …)	All Loans
Age	40.30	38.03	38.18	38.56	38.59
FICO	640.5	638.2	639.8	680.3	674.6
Positive Rental History (%)	14.89	100	11.10	10.78	11.28
First-Time Buyer (%)	84.81	95.77	89.72	81.33	82.36
Loan-to-Value Ratio (%)	94.49	96.07	95.84	94.34	94.50
Back End Ratio (%)	41.05	45.74	46.24	45.20	45.15
Borrower Assets ($)	29,369	14,837	18,414	36,553	34,372
Annual Effective Income ($)	80,036	74,511	74,919	99,421	96,134
Original Mortgage Amount ($)	229,019	251,334	250,020	307,195	298,313
Loan Count	17,852	1,727	48,465	412,546	480,806

Source: SFHEDW, purchase endorsements from on or after October 30, 2022.
Note: X represents the threshold for an accept decision under TOTAL, and X–β represents the new threshold under PRH.

Table 2 presents the distribution of endorsements by race and ethnicity. A comparison of columns 2 and 5 shows that the share of Black borrowers who were accepted because of PRH (17%) is much higher than the share of all TOTAL-approved borrowers (12.78%). We observe a similar race distribution when comparing borrowers who were accepted because of PRH with those just above the threshold in column 3. There is a 0.6 percentage point increase in the Black population, a 0.2 percentage point increase in the Hispanic population, and a 0.26 percentage point decrease in the white population, although these differences are small compared with the share for which race is missing (more than 30 percent).

Table 2: Race and Ethnicity of Purchase Endorsements by TOTAL Category

Race (%)	Manually Underwritten	PRH       [X–β, X)	[X, X+β)	[X+β, …)	All Loans
Black	16.19	17.08	16.49	12.17	12.78
Hispanic	11.12	17.60	17.41	17.99	17.68
White	39.59	33.12	33.37	35.21	35.18
Other	1.60	1.97	1.71	2.88	2.71
Missing	31.50	30.23	31.02	31.73	31.65

Source: SFHEDW, purchase endorsements from on or after October 30, 2022.
Note: X represents the threshold for an accept decision under TOTAL, and X–β represents the new threshold under PRH. Race of the primary borrower is used.

Table 3 presents the distribution by gender. A comparison of columns 2 and 5 shows that the share of female borrowers who were accepted because of PRH (36.48%) is approximately 3 percentage points higher than the average of all TOTAL-approved borrowers (33.53%). Columns 2 and 3 show that the share of female borrowers who were accepted because of PRH is approximately 0.8 percentage points higher than those just above the threshold (35.70%).

Table 3: Gender of Purchase Endorsements by TOTAL Category

Gender (%)	Manually Underwritten	PRH  [X–β, X)	[X, X+β)	[X+β, …)	All Loans
Male	38.45	39.14	38.93	40.71	40.44
Female	35.69	36.48	35.70	33.16	33.53
Unknown	25.84	24.38	25.33	26.09	26.03

Source: SFHEDW, purchase endorsements from on or after October 30, 2022.
Note: X represents the threshold for an accept decision under TOTAL, and X–β represents the new threshold under PRH. Gender of the primary borrower is used.

Regression Analysis of PRH Impact on 60-Day Delinquency Probability

Table 4 presents the results of logistic regressions on the probability of 60-day delinquency. To estimate the performance of loans with PRH, we define a categorical variable that represents the interaction between PRH status (as a binary dummy variable) and the TOTAL score range (below the threshold, just above the threshold, and higher than the threshold). The baseline reference is the non-PRH category with a TOTAL score range just above the threshold, denoted as "Non-PRH [X, X+β)." Model 1 solely controls origination month fixed effects, and Model 2 also incorporates state fixed effects. Model 3 further controls for the TOTAL score, and Model 4 further controls for various loan characteristics.

The coefficients on both non-PRH and PRH loans of the high-TOTAL score group (PRH [X+β, …) and Non-PRH [X+β, …)) exhibit similar magnitudes compared with the baseline across all models. This finding suggests that the performance of PRH loans is not statistically different from the performance of non-PRH loans for this group. Furthermore, the default rate of loans just below the threshold that were accepted because of PRH (PRH [X–β, X)) is not statistically different from that of the base category, while holding other loan characteristics constant. Note, however, that the available data are limited; a longer performance history will be necessary to draw definitive conclusions.

Table 4: Regression Results Assessing the Impact of PRH on the Likelihood of 60-Day Delinquency

	Regressions
	1	2	3	4
Non-PRH (…, X)	0.42**	0.43**	0.00	–0.01
Non-PRH [X, X+β) (Base)	0	0	0	0
Non-PRH [X+β, …)	–1.11**	–1.10**	–0.19**	–0.16**
PRH [X–β, X)	0.17	0.19	0.04	0.04
PRH [X, X+β)	0.10	0.12	0.11	0.11
PRH [X+β, …)	–1.10**	–1.09**	–0.18**	–0.14**
Controls
Loan Controls	No	No	No	Yes
TOTAL Score	No	No	Yes	Yes
State Fixed Effects	No	Yes	Yes	Yes
Origination Fixed Effects	Yes	Yes	Yes	Yes
Number of Observations	476,916	476,889	476,889	476,882
Pseudo R2	0.146	0.150	0.179	0.193

Note: Standard errors are in parenthesis. * p<0.05, ** p<0.01, *** p<0.001. Each column in the table represents a distinct logit regression model. X represents the threshold for an accept decision under TOTAL, and X–β represents the new threshold under PRH.
Source: SFHEDW, purchase endorsements from on or after October 30, 2022.