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Allowance for Credit Losses
 12 Months Ended
Dec. 31, 2020
Credit Loss [Abstract]  
Allowance for Credit Losses Allowance for Credit Losses
Prior to the Company’s adoption of ASC 326 on January 1, 2020, the Company maintained an ALLL in accordance with ASC 310 and ASC 450 that covered estimated credit losses on individually evaluated loans that were determined to be impaired, as well as estimated probable incurred losses inherent in the remainder of the loan portfolio. The ALLL was prepared using the information provided by the Company’s credit review process, including internal risk grades for each loan, together with data from peer institutions and economic information gathered from published sources.

The loan portfolio was segmented into groups of loans with similar risk characteristics. Each segment possessing varying degrees of risk based on, among other things, the type of loan, the type of collateral and the sensitivity of the borrower or industry to changes in external factors such as economic conditions. An estimated loss rate calculated using the Company’s historical loss rates, adjusted for current portfolio trends, economic conditions, and other relevant internal and external factors, was applied to each segment’s aggregate loan balances.

The Company’s base ALLL factors were determined by management using the Bank’s annualized actual trailing charge-off data over a full credit cycle with an approximate average loss emergence period of 1 year to 1.6 years. Potential adjustments to those base factors were made for relevant internal and external factors. Those factors included:

•Changes in lending policies and procedures, including underwriting standards and collection, charge-offs and recovery practices;
•Changes in the nature and volume of the loan portfolio, including new types of lending;
•Changes in the experience, ability, and depth of lending management and other relevant staff that may have an impact on our loan portfolio;
•Changes in the volume and severity of adversely classified or graded loans;
•Changes in the quality of our loan review system and the management oversight;
•The existence and effect of any concentrations of credit and changes in the level of such concentrations;
•Changes in national, regional and local economic conditions, including trends in real estate values and the interest rate environment;
•Changes in the value of the underlying collateral for collateral-dependent loans; and
•The effect of external factors, such as competition, legal developments and regulatory requirements on the level of estimated credit losses in our current loan portfolio

For loans risk graded as watch or worse, progressively higher potential loss factors were applied based on migration analysis of risk grading and net charge-offs.

Effective January 1, 2020, the Company accounts for credit losses on loans in accordance with ASC 326 - Financial Instruments - Credit Losses, to determine the ACL. ASC 326 requires the Company to recognize estimates for lifetime losses on loans and off-balance sheet loan commitments at the time of origination or acquisition. The recognition of losses at origination or acquisition represents the Company’s best estimate of the lifetime expected credit loss associated with a loan given the facts and circumstances associated with the particular loan, and involves the use of significant management judgement and estimates, which are subject to change based on management’s on-going assessment of the credit quality of the loan portfolio and changes in economic forecasts used in the model. The Company uses a discounted cash flow model when determining estimates for the ACL for commercial real estate loans and commercial loans, which comprise the majority of the loan portfolio. The Company uses a historical loss rate model for retail loans. The Company also utilizes proxy loan data in its ACL model where the Company’s own historical data is not sufficiently available.
The discounted cash flow model is applied on an instrument-by-instrument basis, and for loans with similar risk characteristics, to derive estimates for the lifetime ACL for each loan. The discounted cash flow methodology relies on several significant components essential to the development of estimates for future cash flows on loans and off-balance sheet loan commitments. These components consist of: (i) the estimated probability of default, (ii) the estimated loss given default, which represents the estimated severity of the loss when a loan is in default, (iii) estimates for prepayment activity on loans, and (iv) the estimated exposure to the Company at default (“EAD”). These components are also heavily influenced by changes in economic forecasts employed in the model over a reasonable and supportable period. The Company’s ACL methodology for off-balance sheet loan commitments also includes assumptions concerning the probability an unfunded commitment will be drawn upon by the borrower. These assumptions are based on the Company’s historical experience.

The Company’s discounted cash flow ACL model for commercial real estate and commercial loans uses internally derived estimates for prepayments in determining the amount and timing of future contractual cash flows to be collected. The estimate of future cash flows also incorporates estimates for contractual amounts the Company believes may not be collected, which are based on assumptions for PD, LGD, and EAD. EAD is the estimated outstanding balance of the loan at the time of default. It is determined by the contractual payment schedule and expected payment profile of the loan, incorporating estimates for expected prepayments and future draws on revolving credit facilities. The Company discounts cash flows using the effective interest rate on the loan. The effective interest rate represents the contractual rate on the loan; adjusted for any purchase premiums or discounts, and deferred fees and costs associated with the origination of the loan. The Company has made an accounting policy election to adjust the effective interest rate to take into consideration the effects of estimated prepayments. The ACL for loans is determined by measuring the amount by which a loan’s amortized cost exceeds its discounted cash flows.

Probability of Default

The PD for commercial real estate loans is based largely on a model provided by a third party, using proxy loan information. The PDs generated by this model are reflective of current and expected changes in economic conditions and conditions in the commercial real estate market, and how they are expected to impact loan level and property level attributes, and ultimately the likelihood of a default event occurring. Significant loan and property level attributes include: loan-to-value ratios, debt service coverage, loan size, loan vintage and property types.

The PD for commercial loans is based on an internally developed PD rating scale that assigns PDs to individual loans based on the Company’s internal risk grade for each loan. This internally developed PD rating scale is based on a combination of the Company’s own historical data and observed historical data from the Company’s peers, which consist of banks that management believes align with the Company’s business profile. As credit risk grades change for loans in the commercial segment, the PD assigned to them also changes. As with commercial real estate loans, the PD for commercial loans is also impacted by current and expected economic conditions.

The Company considers loans to be in default when they are 90 days or more past due and still accruing or placed on nonaccrual status.
Loss Given Default

LGDs for commercial real estate loans are derived from a third party, using proxy loan information, and are based on loan and property level characteristics for loans in the Company’s loan portfolio, such as: loan-to-values, estimated time to resolution, property size, and current and estimated future market price changes for underlying collateral. The LGD is highly dependent upon estimated loan-to-value ratios, and incorporates estimates for the expense associated with managing the loan through to resolution. LGDs also incorporate an estimate for the loss severity associated with loans where the borrower fails to meet their debt obligation at maturity, such as through a balloon payment or the refinancing of the loan through another lender. External factors that have an impact on LGDs include: changes in the CRE Price Index, GDP growth rate, unemployment rates, and the Moody’s Baa rating corporate debt interest rate spread. LGDs are applied to each loan in the commercial real estate portfolio, and in conjunction with the PD, produce estimates for net cash flows not expected to be collected over the estimated term of the loan.

LGDs for commercial loans are also derived from a third party that has a considerable database of credit related information specific to the financial services industry and the type of loans within this segment, and is used to generate annual default information for commercial loans. These proxy LGDs are dependent upon data inputs such as: credit quality, borrower industry, region, borrower size, and debt seniority. LGDs are then applied to each loan in the commercial portfolio, and in conjunction with the PD, produce estimates for net cash flows not expected to be collected over the estimated term of the loan.

Historical Loss Rates for Retail Loans

The historical loss rate model for retail loans is derived from a third party that has a considerable database of credit related information for retail loans. Key loan level attributes and economic drivers in determining the loss rate for retail loans include FICO scores, vintage, as well as geography, unemployment rates, and changes in consumer real estate prices.

Economic Forecasts

GAAP requires the Company to develop reasonable and supportable forecasts of future conditions, and estimate how those forecasts are expected to impact a borrower’s ability to satisfy their obligation to the Bank and the ultimate collectability of future cash flows over the life of the loan. The Company uses economic forecast scenarios from an independent third party, Moody’s Analytics, in its estimation of a borrower’s ability to repay a loan in future periods. These scenarios are based on past events, current conditions, and the likelihood of future events occurring. These scenarios typically are comprised of: (1) a base-case scenario, (2) an upside scenario, representing slightly better economic conditions than currently experienced and, (3) a downside scenario, representing recessionary conditions. Management periodically evaluates economic forecast scenarios and may decide that a particular economic scenario or a combination of probability-weighted economic scenarios should be used in the Company’s ACL model. The economic scenarios chosen for the model, the extent to which more than one scenario is used, and the weights that are assigned to them, are based on the Company’s estimate of the probability of each scenario occurring, which is based in part on analysis performed by an independent third-party. Economic scenarios chosen, as well as the assumptions within those scenarios, and whether to use a probability-weighted multiple scenario approach, can vary from one period to the next based on changes in current and expected economic conditions, and due to the occurrence of specific events such as the on-going COVID-19 pandemic. The Company recognizes the non-linearity of credit losses relative to economic performance and thus the Company believes consideration of and, if appropriate under the circumstances, use of multiple probability-weighted economic scenarios is appropriate in estimating credit losses over the forecast period. This approach is based on certain assumptions. The first assumption is that no single forecast of the economy, however detailed or complex, is completely accurate over a reasonable forecast time-frame, and is subject to revisions over time. By considering multiple scenario outcomes and assigning reasonable probability weightings to them, some of the uncertainty associated with a single scenario approach, the Company believes, is mitigated.
As of January 1, 2020, upon the adoption of ASC 326, the Company’s ACL model used three probability-weighted forecast scenarios representing a base-case scenario, an upside scenario, and a downside scenario. The weightings assigned to each scenario were as follows: the base-case scenario, or most likely scenario, was assigned a weighting of 40%, while the upside and downside scenarios were each assigned weightings of 30%. As of December 31, 2020, the Company’s ACL model used the same three probability weighted scenarios, updated for current expected economic conditions, including the current and estimated future impact associated with the on-going COVID-19 pandemic. The Company evaluated the weightings of each economic scenario in the current period with the assistance of Moody's Analytics, and determined the current weightings of 40% for the base-case scenario, and 30% for each of the upside and downside scenarios appropriately reflect the likelihood of outcomes for each scenario given the current economic environment.

The Company currently forecasts economic conditions over a two-year period, which we believe is a reasonable and supportable period. Beyond the point which the Company can provide for a reasonable and supportable forecast, economic variables revert to historical long-term averages. The Company has reflected this reversion over a period of three years in each of its economic scenarios used to generate the overall probability-weighted forecast. Changes in economic forecasts impact the PD, LGD, and EAD for each loan, and therefore influence the amount of future cash flows from each loan the Company does not expect to collect.

The Company derives the economic forecasts it uses in its ACL model from Moody's Analytics that has a large team of economists, data-base managers, and operational engineers with a history of producing monthly economic forecasts for over 25 years. The forecasts produced by this third party have been widely used by banks, credit unions, government agencies, and real estate developers. These economic forecasts cover all states and metropolitan areas in the Unites States, and reflect changes in economic variables such as: GDP growth, interest rates, employment rates, changes in wages, retail sales, industrial production, metrics associated with the single-family and multifamily housing markets, vacancy rates, changes in equity market prices, and energy markets.

It is important to note that the Company’s ACL model relies on multiple economic variables, which are used under several economic scenarios. Although no one economic variable can fully demonstrate the sensitivity of the ACL calculation to changes in the economic variables used in the model, the Company has identified certain economic variables that have significant influence in the Company’s model for determining the ACL. As of December 31, 2020, the Company’s ACL model incorporated the following assumptions for key economic variables in the base-case and downside scenarios:

Base-case Scenario:

•CRE Price Index experiences declines throughout 2021, with the estimated annualized rate of decline slowing from approximately -28% in early 2021 to approximately -13% by the end of 2021. This scenario also assumes the CRE Price Index returns to modest levels of growth by the second quarter of 2022.
•U.S. real GDP experiences modestly increasing levels of growth throughout 2021 in the range of 2-6% on an annualized basis. This scenario also assumes modest levels of growth in 2022 at an approximate annualized rate of 4%.
•U.S. unemployment of approximately 7% throughout 2021, followed by modest declines throughout 2022 to an approximate level of 5% by the end of 2022.

Upside Scenario:

•CRE Price Index experiences declines throughout 2021, with the estimated annualized rate of decline slowing from approximately -16% in early 2021 to approximately -9% by the end of 2021. This scenario also assumes the CRE Price Index returns to modest levels of growth by the second quarter of 2022.
•U.S. real GDP experiences modestly increasing levels of growth throughout 2021 in the range of 5-8% on an annualized basis. This scenario also assumes modest levels of annualized growth in 2022 in an approximate range of 2-4%.
•U.S. unemployment declining from approximately 6% to approximately 5% by the end of 2021. This scenario also assumes the rate of unemployment continues to decline throughout 2022 to an approximate level of 4% by the end of 2022.
Downside Scenario:

•CRE Price Index experiences significant declines throughout 2021, with the estimated annualized rate of decline slowing from approximately -36% in early 2021 to approximately -26% by the end of 2021. This scenario also assumes the CRE Price Index returns to modest levels of growth by the third quarter of 2022.
•U.S. real GDP experiences slowing rates of decline through the third quarter of 2020, from an approximate rate of -5% to -0.3%, before returning to growth in the fourth quarter of 2022. This scenario also assumes modest levels of annualized growth in 2022 in an approximate range of 2-4%.
•Increasing levels of U.S. unemployment throughout 2021, with the rate of unemployment increasing each quarter to approximately 10% by the end of 2021. This scenario also assumes the rate of unemployment remains elevated in 2022, but begins to fall to approximately 9% by the end of 2022.

Qualitative Adjustments

The Company recognizes that historical information used as the basis for determining future expected credit losses may not always, by itself, provide a sufficient basis for determining future expected credit losses. The Company, therefore, periodically considers the need for qualitative adjustments to the ACL. Qualitative adjustments may be related to and include, but not be limited to, factors such as: (i) management’s assessment of economic forecasts used in the model and how those forecasts align with management’s overall evaluation of current and expected economic conditions, (ii) organization specific risks such as credit concentrations, collateral specific risks, regulatory risks, and external factors that may ultimately impact credit quality, (iii) potential model limitations such as limitations identified through back-testing, and other limitations associated with factors such as underwriting changes, acquisition of new portfolios, and changes in portfolio segmentation, and (iv) management’s overall assessment of the adequacy of the ACL, including an assessment of model data inputs used to determine the ACL. As of December 31, 2020, qualitative adjustments included in the ACL totaled $10.0 million. These adjustments relate to management’s overall assessment of the adequacy of the ACL and the potential for the model, as of December 31, 2020, to underestimate the effects of current changes in asset quality. Management reviews the need for and appropriate level of qualitative adjustments on a quarterly basis, and as such, the amount and allocation of qualitative adjustments may change in future periods.
The following table provides the allocation of the ACL for loans held for investment as well as the activity in the ACL attributed to various segments in the loan portfolio as of, and for the period indicated:


For the Year Ended December 31, 2020
 Beginning ACL Balance (1)
 Adoption of ASC 326  Initial ACL Recorded for PCD Loans  Charge-offs  Recoveries Provision for Credit Losses  Ending
ACL Balance
(Dollars in thousands)
Investor loans secured by real estate
CRE non-owner occupied$1,899 $8,423 $3,025 $(839)$44 $36,624 $49,176 
Multifamily729 9,174 8,710 — — 43,921 62,534 
Construction and land4,484 (124)2,051 (539)— 6,563 12,435 
SBA secured by real estate1,915 (1,401)— (705)34 5,316 5,159 
Business loans secured by real estate
CRE owner-occupied2,781 20,166 3,766 (1,739)59 25,484 50,517 
Franchise real estate secured592 5,199 — (932)— 6,592 11,451 
SBA secured by real estate2,119 2,207 235 (338)147 2,197 6,567 
Commercial loans
Commercial and industrial13,857 87 2,325 (6,891)1,818 35,768 46,964 
Franchise non-real estate secured5,816 9,214 — (6,731)866 11,360 20,525 
SBA non-real estate secured445 218 924 (899)14 293 995 
Retail loans
Single family residential655 541 206 (106)2 (94)1,204 
Consumer loans406 1,982 — (139)4 (1,762)491 
Totals$35,698 $55,686 $21,242 $(19,858)$2,988 $172,262 $268,018 
______________________________
(1) Beginning ACL balance represents the ALLL accounted for under ASC 450 and ASC 310, which is reflective of probable incurred losses as of the balance sheet date.
The following table provides the allocation of the ALLL for loans held for investment as well as the activity attributed to various segments in the loan portfolio as of, and for the period indicated, as determined in accordance with ASC 450 and ASC 310, prior to the adoption of ASC 326:

For the Year Ended December 31, 2019
Beginning ALLL BalanceCharge-offsRecoveriesProvision for Credit LossesEnding
ALLL Balance
(Dollars in thousands)
Investor loans secured by real estate
CRE non-owner-occupied$1,624 $(625)$— $900 $1,899 
Multifamily740 — — (11)729 
Construction and land5,964 — — (1,480)4,484 
SBA secured by real estate1,827 (742)— 830 1,915 
Business loans secured by real estate
CRE owner-occupied1,908 (125)46 952 2,781 
Franchise real estate secured743 (1,377)— 1,226 592 
SBA secured by real estate1,824 (908)10 1,193 2,119 
Commercial loans
Commercial and industrial13,695 (2,318)189 2,291 13,857 
Franchise non-real estate secured6,066 (1,154)18 886 5,816 
SBA non-real estate secured654 (588)68 311 445 
Retail loans
Single family residential808 — 2 (155)655 
Consumer loans219 (16)11 192 406 
Totals$36,072 $(7,853)$344 $7,135 $35,698 
For the Year Ended December 31, 2018
Beginning ALLL BalanceCharge-offsRecoveriesProvision for Credit LossesEnding
ALLL Balance
(Dollars in thousands)
Investor loans secured by real estate
CRE non-owner-occupied$1,273 $— $— $351 $1,624 
Multifamily614 — — 126 740 
Construction and land5,565 — — 399 5,964 
SBA secured by real estate1,396 — — 431 1,827 
Business loans secured by real estate
CRE owner-occupied923 (33)47 971 1,908 
Franchise real estate secured602 — — 141 743 
SBA secured by real estate901 — — 923 1,824 
Commercial loans
Commercial and industrial11,018 (1,411)698 3,390 13,695 
Franchise non-real estate secured5,191 — — 875 6,066 
SBA non-real estate secured594 (102)169 (7)654 
Retail loans
Single family residential804 — 13 (9)808 
Consumer loans55 (409)8 565 219 
Totals$28,936 $(1,955)$935 $8,156 $36,072 

The increase in the ACL for loans held-for-investment during the year ended December 31, 2020 of $232.3 million is reflective of $172.3 million in provisions for credit losses, net charge-offs of $16.9 million, the establishment of $21.2 million in net ACL for PCD loans acquired in the Opus acquisition, and a $55.7 million adjustment to the ACL associated with the Company’s January 1, 2020 adoption of ASC 326, which was recorded through a cumulative effect adjustment to retained earnings. The provision for credit losses in 2020 is inclusive of $75.9 million related to the initial ACL required for the acquisition of non-PCD loans in the Opus acquisition. Under ASC 326, the Company is required to record an ACL for estimates of life-time credit losses on loans at the time of acquisition. For non-PCD loans, the initial ACL is established through a charge to provision for credit losses at the time of acquisition. However, the ACL for PCD loans is established through an adjustment to the loan’s purchase price (or initial fair value). Excluding the impact of the Opus acquisition, the provision for credit losses of $96.4 million for the year ended December 31, 2020 is also reflective of unfavorable economic forecasts employed in the Company’s ACL model driven by the on-going COVID-19 pandemic.

As previously mentioned, prior to the Company’s adoption of ASC 326 on January 1, 2020, the Company maintained an allowance for loan and lease losses in accordance with ASC 450 and ASC 310, which required the Company to measure credit losses on loans using a probable incurred loss model. The probable incurred loss model was reflective of estimates for loan losses incurred and inherent in the loan portfolio as of the balance sheet date, and did not reflect current estimates of future expected credit losses over the lives of the Company’s loans, as now required by ASC 326.

For the years ended December 31, 2019 and 2018, the Company recorded provisions for loan losses for loans held-for-investment of $7.1 million and $8.2 million, respectively. Provisions for loan losses in 2019 reflected the replenishment of ALLL as a result of charge-offs. Provisions for loan losses in 2018 were largely driven by the growth in the Company’s loan portfolio primarily attributable to the acquisition of Grandpoint.
Allowance for Credit Losses for Off-Balance Sheet Commitments

The Company maintains an allowance for credit losses on off-balance sheet commitments related to unfunded loans and lines of credit, which is included in other liabilities of the consolidated balance sheets. Upon the Company’s adoption of ASC 326 on January 1, 2020, the Company applies an expected credit loss estimation methodology for off-balance sheet commitments. This methodology is commensurate with the methodology applied to each respective segment of the loan portfolio in determining the ACL for loans held-for-investment. The loss estimation process includes assumptions for the probability that a loan will fund, as well as the expected amount of funding. These assumptions are based on the Company’s own historical internal loan data.

The allowance for off-balance sheet commitments was $31.1 million at December 31, 2020 and $3.3 million at December 31, 2019. The change in the allowance for off-balance sheet commitments can be attributed to several factors, including: (i) an $8.3 million increase in the first quarter of 2020 attributed to the Company’s adoption of ASC 326, (ii) an $8.6 million provision for credit losses in the second quarter of 2020 related to the required initial ACL on off-balance sheet loan commitments that the Company was required to establish at the time of acquisition of Opus, and (iii) an $11.0 million in provision for credit losses during 2020 related primarily to the deterioration in economic forecasts employed in the Company’s CECL model.

For the year ended December 31, 2019, the Company recorded a recapture provision for off-balance sheet commitments of $1.4 million. For the year ended December 31, 2018, the Company recorded a provision for unfunded loan commitments of $97,000. The recapture of and provision for off-balance sheet loan commitments in 2019 and 2018 can be attributed to changes in the level of unfunded loan commitments during those periods.

The following table presents loans individually and collectively evaluated for impairment and their respective ALLL allocation at December 31, 2019 as determined in accordance with ASC 450 and ASC 310, prior to the adoption of ASC 326:
December 31, 2019
Loans Evaluated Individually for ImpairmentALLL Attributed to Individually Evaluated LoansLoans Evaluated Collectively for ImpairmentALLL Attributed to Collectively Evaluated Loans
(Dollars in thousands)
Investor loans secured by real estate
CRE non-owner-occupied$1,088 $— $2,069,053 $1,899 
Multifamily— — 1,575,726 729 
Construction and land— — 438,786 4,484 
SBA secured by real estate390 — 68,041 1,915 
Business loans secured by real estate
CRE owner-occupied— — 1,846,554 2,781 
Franchise real estate secured— — 353,240 592 
SBA secured by real estate1,517 — 86,864 2,119 
Commercial loans
Commercial and industrial7,529 — 1,385,741 13,857 
Franchise non-real estate secured10,834 — 553,523 5,816 
SBA non-real estate secured1,118 — 16,308 445 
Retail loans
Single family residential366 — 254,658 655 
Consumer loans— — 50,975 406 
Totals$22,842 $— $8,699,469 $35,698 
The following table presents PD bands for commercial real estate and commercial loan segments of the loan portfolio as of the date indicated.
Commercial Real Estate Term Loans by Vintage
20202019201820172016PriorRevolvingRevolving Converted to Term During the PeriodTotal
(Dollars in thousands)
December 31, 2020
Investor loans secured by real estate
CRE non-owner-occupied
0% - 5.00%$261,885 $491,522 $431,791 $266,942 $254,527 $763,101 $11,114 $— $2,480,882 
>5.00% - 10.00%4,016 34,360 5,794 10,558 16,961 33,734 — — 105,423 
Greater than 10%— 25,844 11,480 10,517 10,782 29,598 559 — 88,780 
Multifamily
0% - 5.00%950,089 1,610,011 878,233 634,268 349,549 516,452 — — 4,938,602 
>5.00% - 10.00%38,892 59,500 12,181 19,751 10,917 13,606 — — 154,847 
Greater than 10%38,663 9,963 11,339 12,479 3,814 1,229 420 — 77,907 
Construction and Land
0% - 5.00%55,785 40,860 4,604 11,238 — 6,412 784 — 119,683 
>5.00% - 10.00%1,123 41,046 9,197 3,601 — 260 — — 55,227 
Greater than 10%401 62,853 59,512 3,786 20,531 — — — 147,083 
SBA secured by real estate
0% - 5.00%496 10,400 12,558 14,497 7,078 10,032 — — 55,061 
>5.00% - 10.00%— — — 1,012 — — — — 1,012 
Greater than 10%— 158 589 — — 511 — — 1,258 
Total investor loans secured by real estate$1,351,350 $2,386,517 $1,437,278 $988,649 $674,159 $1,374,935 $12,877 $— $8,225,765 
Business loans secured by real estate
CRE owner-occupied
0% - 5.00%$286,745 $367,269 $274,512 $295,809 $202,282 $422,614 $10,393 $246 $1,859,870 
>5.00% - 10.00%8,769 42,310 60,222 28,421 23,875 44,855 3,875 — 212,327 
Greater than 10%— 16,096 5,376 7,459 4,263 8,409 250 — 41,853 
Franchise real estate secured
0% - 5.00%37,262 79,926 65,619 96,672 19,046 22,927 — — 321,452 
>5.00% - 10.00%7,587 1,650 3,274 327 5,627 4,093 — — 22,558 
Greater than 10%442 1,512 — — 1,968 — — — 3,922 
SBA secured by real estate
0% - 5.00%3,253 7,637 11,840 15,069 5,707 18,742 — — 62,248 
>5.00% - 10.00%— — 768 989 2,780 4,882 — — 9,419 
Greater than 10%— — 1,384 1,987 1,514 3,043 — — 7,928 
Total business loans secured by real estate$344,058 $516,400 $422,995 $446,733 $267,062 $529,565 $14,518 $246 $2,541,577 
Commercial Real Estate Term Loans by Vintage
20202019201820172016PriorRevolvingRevolving Converted to Term During the PeriodTotal
(Dollars in thousands)
December 31, 2020
Commercial Loans
Commercial and industrial
0% - 5.00%$70,233 $205,395 $99,178 $193,046 $36,957 $62,682 $394,124 $5,051 $1,066,666 
>5.00% - 10.00%49,883 50,743 35,813 13,427 12,922 13,948 322,123 2,469 501,328 
Greater than 10%7,701 7,540 29,078 4,485 4,574 8,350 136,253 2,859 200,840 
Franchise non-real estate secured
0% - 5.00%21,409 145,392 88,171 38,010 21,956 23,479 — 502 338,919 
>5.00% - 10.00%6,198 15,754 5,454 8,164 18,415 3,626 — — 57,611 
Greater than 10%— 16,836 6,612 18,655 1,638 3,165 1,361 — 48,267 
SBA not secured by real estate
0% - 5.00%407 2,257 910 1,078 441 2,782 — — 7,875 
>5.00% - 10.00%— — 648 1,596 169 1,652 — 259 4,324 
Greater than 10%— 83 357 1,856 340 415 707 — 3,758 
Total commercial loans$155,831 $444,000 $266,221 $280,317 $97,412 $120,099 $854,568 $11,140 $2,229,588 
A significant driver in the ACL for loans in the investor real estate secured and business real estate secured segments is loan to value (“LTV”). The following table summarizes the amortized cost of loans in these segments by current estimated LTV and by year of origination as of the date indicated:
Term Loans by Vintage
20202019201820172016PriorRevolvingRevolving Converted to Term During the PeriodTotal
(Dollars in thousands)
December 31, 2020
Investor loans secured by real estate
CRE non-owner-occupied
55% and below$138,007 $229,272 $182,385 $136,355 $189,848 $588,230 $11,114 $— $1,475,211 
>55-65%101,434 217,210 92,015 130,024 78,470 204,161 559 — 823,873 
>65-75%26,460 102,494 169,878 18,876 13,952 29,506 — — 361,166 
Greater than 75%— 2,750 4,787 2,762 — 4,536 — — 14,835 
Multifamily
55% and below218,833 345,519 294,464 233,997 84,530 269,906 — — 1,447,249 
>55-65%381,737 731,408 381,282 215,170 152,066 189,151 420 — 2,051,234 
>65-75%427,074 583,078 215,389 215,452 127,684 66,457 — — 1,635,134 
Greater than 75%— 19,469 10,618 1,879 — 5,773 — — 37,739 
Construction and land
55% and below57,309 105,308 36,068 18,625 20,531 6,672 784 — 245,297 
>55-65%— 36,113 23,770 — — — — — 59,883 
>65-75%— 3,338 13,475 — — — — — 16,813 
Greater than 75%— — — — — — — — — 
SBA secured by real estate
55% and below— 2,066 649 673 317 778 — — 4,483 
>55-65%— 2,427 1,639 4,008 879 4,354 — — 13,307 
>65-75%— 3,897 3,882 3,482 4,519 1,884 — — 17,664 
Greater than 75%496 2,168 6,977 7,346 1,363 3,527 — — 21,877 
Total investor loans secured by real estate$1,351,350 $2,386,517 $1,437,278 $988,649 $674,159 $1,374,935 $12,877 $— $8,225,765 
Business loan secured by real estate
CRE owner-occupied
55% and below$96,803 $160,605 $157,868 $179,791 $131,795 $328,188 $14,518 $246 $1,069,814 
>55-65%72,044 91,028 98,176 94,712 65,120 90,548 — — 511,628 
>65-75%71,692 152,920 79,106 43,832 31,303 31,493 — — 410,346 
Greater than 75%54,975 21,122 4,960 13,354 2,202 25,649 — — 122,262 
Franchise real estate secured
55% and below20,801 10,470 13,864 20,956 9,189 16,213 — — 91,493 
>55-65%2,689 9,955 16,001 19,102 6,855 2,333 — — 56,935 
>65-75%19,349 51,719 23,258 9,153 10,597 7,236 — — 121,312 
Greater than 75%2,452 10,944 15,770 47,788 — 1,238 — — 78,192 
SBA secured by real estate
55% and below1,825 1,626 5,332 5,495 3,615 13,582 — — 31,475 
>55-65%246 513 1,795 1,094 3,586 5,448 — — 12,682 
>65-75%264 3,142 1,515 3,968 1,586 4,043 — — 14,518 
Greater than 75%918 2,356 5,350 7,488 1,214 3,594 — — 20,920 
Total business loans secured by real estate$344,058 $516,400 $422,995 $446,733 $267,062 $529,565 $14,518 $246 $2,541,577 
The following table presents FICO bands for the retail segment of the loan portfolio as of the date indicated:
Term Loans by Vintage
20202019201820172016PriorRevolvingRevolving Converted to Term During the PeriodTotal
(Dollars in thousands)
December 31, 2020
Retail Loans
Single family residential
Greater than 740$10,794 $6,531 $12,679 $8,846 $28,222 $81,838 $19,588 $— $168,498 
>680 - 740— 1,183 1,303 4,732 2,614 15,624 6,685 — 32,141 
>580 - 680— — — 461 3,132 7,473 864 — 11,930 
Less than 580— — — — — 19,970 35 — 20,005 
Consumer loans
Greater than 74052 69 31 22 1 2,609 2,198 — 4,982 
>680 - 740— 35 6 3 — 469 1,227 — 1,740 
>580 - 680— 15 — — 1 95 56 — 167 
Less than 580— — — — — 13 27 — 40 
Total retail loans$10,846 $7,833 $14,019 $14,064 $33,970 $128,091 $30,680 $— $239,503