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Allowance for Credit Losses
9 Months Ended
Sep. 30, 2020
Provision for Loan and Lease Losses [Abstract]  
Allowance for Credit Losses Allowance for Credit Losses
 
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 unfunded 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, and 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 unfunded 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 unfunded 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, purchase 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 based on the Company’s internal risk grades for loans. 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 our 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 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 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 are 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.

Forecasts

U.S. 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 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 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 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 September 30, 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 use of three probability-weighted scenarios in the third quarter of 2020 is consistent with the approach used in the Company’s ACL model during the second quarter of 2020. 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 their 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 for 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 September 30, 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 decreases by an approximate annualized rate of 16% through the remainder of 2020 with the rate of decline slowing in Q1 2021 to 10%, before returning to growth by the second quarter of 2021.
A modest increase in real GDP of an approximate 3% annualized rate in Q4 2020, followed by increasing levels of real GDP growth between 3-6% during 2021.
Elevated levels of U.S. unemployment at approximately 9% in Q4 2020, followed by modest declines throughout 2021 to an approximate level of 8% by the end of 2021.
Upside Scenario:

CRE Price Index annualized growth rate is unchanged in Q4 2020, before returning to growth by the second quarter of 2021.
An approximate annualized increase in real GDP of 8% in Q4 2020, followed by decelerating levels of growth in 2021 from approximately 7% to 5% by the end of 2021.
Elevated levels of U.S. unemployment at approximately 9% for Q4 2020, followed by declines in unemployment throughout 2021 to an approximate level of 6% by the end of 2021.

Downside Scenario:

CRE Price Index decreases by an approximate annualized rate of 25% in Q4 2020, with the rate of decline decreasing throughout 2021, before returning to modest growth by Q4 2021.
A decrease in real GDP of an approximate annualized rate of 4% in Q4 2020, followed by declines of 3% and 2% in Q1 and Q2 2021, respectively, before returning to growth in Q3 2021.
Elevated levels of U.S. unemployment at approximately 10% for Q4 2020, followed by unemployment of approximately 11% throughout 2021.

Qualitative Adjustments

The Company recognizes that historical information used as the basis for determining future expected credit losses may not always, by themselves, 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 September 30, 2020, qualitative adjustments included in the ACL totaled $15.0 million. These adjustments relate to potential limitations in the model. Management determined through additional review that certain key model drivers are potentially underestimating the impact of the on-going COVID-19 pandemic may have on small and medium sized businesses, and may not be fully reflecting the potential for a more turbulent economic recovery. In addition, the qualitative adjustment relates to, in part, the lack of additional economic stimulus from the federal government as of September 30, 2020. Many economists point to the need for additional stimulus to help ensure the recovery in economic conditions, as a whole, does not begin to wane. 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:

Three Months Ended September 30, 2020
 Beginning ACL Balance  Charge-offs  Recoveries Provision for Credit Losses  Ending
ACL Balance
(Dollars in thousands)
Investor loans secured by real estate
CRE non-owner occupied$63,007 $(443)$— $(8,459)$54,105 
Multifamily63,511 — — 3,825 67,336 
Construction and land18,804 (377)— (2,870)15,557 
SBA secured by real estate2,010 (145)34 3,428 5,327 
Business loans secured by real estate
CRE owner-occupied48,213 (1,739)21 2,171 48,666 
Franchise real estate secured13,060 — — (1,072)11,988 
SBA secured by real estate4,368 — 76 1,716 6,160 
Commercial loans
Commercial and industrial41,967 (2,437)10 8,374 47,914 
Franchise non-real estate secured21,676 (207)865 (2,185)20,149 
SBA non-real estate secured600 (10)353 951 
Retail loans
Single family residential1,479 — (238)1,243 
Consumer loans3,576 (129)(341)3,107 
Totals$282,271 $(5,487)$1,017 $4,702 $282,503 

Nine Months Ended September 30, 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 $(830)$— $41,588 $54,105 
Multifamily729 9,174 8,710 — — 48,723 67,336 
Construction and land4,484 (124)2,051 (377)— 9,523 15,557 
SBA secured by real estate1,915 (1,401)— (699)34 5,478 5,327 
Business loans secured by real estate
CRE owner-occupied2,781 20,166 3,766 (1,739)44 23,648 48,666 
Franchise real estate secured592 5,199 — — — 6,197 11,988 
SBA secured by real estate2,119 2,207 235 (315)147 1,767 6,160 
Commercial loans
Commercial and industrial13,857 87 2,325 (5,213)37 36,821 47,914 
Franchise non-real estate secured5,816 9,214 — (1,434)865 5,688 20,149 
SBA non-real estate secured445 218 924 (803)13 154 951 
Retail loans
Single family residential655 541 206 (62)(100)1,243 
Consumer loans406 1,982 — (137)854 3,107 
Totals$35,698 $55,686 $21,242 $(11,609)$1,145 $180,341 $282,503 
______________________________
(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 Three Months Ended September 30, 2019
Beginning ALLL BalanceCharge-offsRecoveriesProvision for Credit LossesEnding
ALLL Balance
(Dollars in thousands)
Investor loans secured by real estate
CRE non-owner-occupied$1,765 $(86)$— $199 $1,878 
Multifamily705 — — 10 715 
Construction and land5,408 — — (617)4,791 
SBA secured by real estate1,322 — — 468 1,790 
Business loans secured by real estate
CRE owner-occupied2,299 — 257 2,564 
Franchise real estate secured579 — — (12)567 
SBA secured by real estate1,611 (61)21 539 2,110 
Commercial loans
Commercial and industrial13,796 (290)54 (664)12,896 
Franchise non-real estate secured6,186 (995)— 975 6,166 
SBA non-real estate secured430 (82)41 92 481 
Retail loans
Single family residential704 — (14)691 
Consumer loans221 (11)132 351 
Totals$35,026 $(1,525)$134 $1,365 $35,000 
For the Nine Months Ended September 30, 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 $(574)$— $828 $1,878 
Multifamily740 — — (25)715 
Construction and land5,964 — — (1,173)4,791 
SBA secured by real estate1,827 (721)— 684 1,790 
Business loans secured by real estate
CRE owner-occupied1,908 — 31 625 2,564 
Franchise real estate secured743 (1,376)— 1,200 567 
SBA secured by real estate1,824 (315)21 580 2,110 
Commercial loans
Commercial and industrial13,695 (985)168 18 12,896 
Franchise non-real estate secured6,066 (1,155)— 1,255 6,166 
SBA non-real estate secured654 (326)45 108 481 
Retail loans
Single family residential808 — (119)691 
Consumer loans219 (16)10 138 351 
Totals$36,072 $(5,468)$277 $4,119 $35,000 
The increase in the ACL for loans held for investment during the three months ended September 30, 2020 of $232,000 is reflective of a $4.7 million in provision for credit losses and $4.5 million in net charge-offs. The provision for credit losses for the three months ended September 30, 2020 is reflective of unfavorable, but improving economic forecasts used in the Company’s ACL model. The change in the ACL for the nine months ended September 30, 2020 of $246.8 million is reflective of a $55.7 million addition associated with the Company’s adoption of ASC 326 on January 1, 2020, which was recorded through a cumulative effect adjustment to retained earnings, as well as a $180.3 million provision for credit losses on loans, net charge-offs of $10.5 million, and the establishment of $21.2 million in net ACL for PCD loans previously mentioned. The provision for credit losses of $180.3 million during the nine months ended September 30, 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 for the nine months ended September 30, 2020 is also reflective of unfavorable economic forecasts used in the Company’s ACL model driven by the COVID-19 pandemic.

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. The allowance for off-balance sheet commitments was $21.5 million at September 30, 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) a $8.6 million provision for credit losses in the second quarter of 2020 related to the initial ACL on off-balance sheet loan commitments that the Company was required to establish at the time of acquisition of Opus, and (iii) a $1.4 million in provision for credit losses for the first nine months of 2020 related primarily to the deterioration in economic forecasts, primarily in the second quarter of 2020, used in the Company’s CECL model. The total provision for credit losses for off-balance sheet commitments reflected a recapture of $492,000 and a provision of $10.0 million for the three and nine months ended September 30, 2020, respectively. The reversal of provision for credit losses for off-balance sheet commitments for the three months ended September 30, 2020 can be attributed to lower outstanding unfunded balance in certain loan segments where a higher reserve allocation has been assigned.

The Company applies an expected credit loss estimation methodology for off-balance sheet commitments that 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 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
September 30, 2020(Dollars in thousands)
Investor loans secured by real estate
CRE non-owner-occupied
0% - 5.00%$188,404 $543,940 $374,636 $265,887 $266,967 $792,295 $10,788 $— $2,442,917 
>5.00% - 10.00%— 7,015 102,566 10,874 17,282 25,791 243 — 163,771 
Greater than 10%2,297 15,900 202 37,864 6,447 37,973 559 — 101,242 
Multifamily
0% - 5.00%630,900 1,709,927 935,659 723,886 399,890 618,503 574 — 5,019,339 
>5.00% - 10.00%4,251 11,286 14,944 14,708 2,836 2,102 — — 50,127 
Greater than 10%3,198 14,022 14,137 12,513 10,167 18,566 — — 72,603 
Construction and Land
0% - 5.00%24,862 56,858 12,550 22,362 — 6,448 — — 123,080 
>5.00% - 10.00%— 36,289 10,750 466 — — — — 47,505 
Greater than 10%378 52,562 82,297 10,195 19,932 1,549 374 — 167,287 
SBA secured by real estate
0% - 5.00%494 10,412 12,584 15,540 7,111 10,204 — — 56,345 
>5.00% - 10.00%— — — — — — — — — 
Greater than 10%— 163 591 — — 511 — — 1,265 
Total investor loans secured by real estate$854,784 $2,458,374 $1,560,916 $1,114,295 $730,632 $1,513,942 $12,538 $— $8,245,481 
Business loans secured by real estate
CRE owner-occupied
0% - 5.00%$202,228 $368,034 $299,843 $296,647 $197,043 $426,513 $520 $— $1,790,828 
>5.00% - 10.00%8,434 37,617 58,314 53,967 39,056 78,666 3,826 — 279,880 
Greater than 10%— 22,362 3,636 7,285 5,793 9,507 497 — 49,080 
Franchise real estate secured
0% - 5.00%19,753 85,363 73,100 102,756 31,512 42,346 — — 354,830 
>5.00% - 10.00%754 — 631 — — — — — 1,385 
Greater than 10%— 2,386 728 — — — — — 3,114 
SBA secured by real estate
0% - 5.00%2,643 7,658 13,375 15,993 6,150 22,077 95 — 67,991 
>5.00% - 10.00%— — 679 1,117 3,493 4,679 — — 9,968 
Greater than 10%— — 22 1,994 914 3,237 — — 6,167 
Total business loans secured by real estate$233,812 $523,420 $450,328 $479,759 $283,961 $587,025 $4,938 $— $2,563,243 
Commercial Real Estate Term Loans by Vintage
20202019201820172016PriorRevolvingRevolving Converted to Term During the PeriodTotal
September 30, 2020(Dollars in thousands)
Commercial Loans
Commercial and industrial
0% - 5.00%$90,745 $289,250 $141,811 $206,524 $49,843 $76,628 $343,482 $4,024 $1,202,307 
>5.00% - 10.00%7,911 48,778 39,716 19,950 18,894 14,807 302,843 2,037 454,936 
Greater than 10%— 6,061 41,726 23,095 5,340 7,178 78,355 1,997 163,752 
Franchise non-real estate secured
0% - 5.00%14,547 193,899 112,760 48,916 45,265 40,753 1,361 511 458,012 
>5.00% - 10.00%5,658 7,722 4,353 4,245 2,025 2,486 — — 26,489 
Greater than 10%— 2,050 957 28,472 — — — — 31,479 
SBA not secured by real estate
0% - 5.00%355 2,299 1,392 1,460 499 2,571 — 268 8,844 
>5.00% - 10.00%— — 308 796 124 1,285 — — 2,513 
Greater than 10%— 85 369 2,479 275 1,449 734 — 5,391 
Total commercial loans$119,216 $550,144 $343,392 $335,937 $122,265 $147,157 $726,775 $8,837 $2,353,723 
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
September 30, 2020(Dollars in thousands)
Investor loans secured by real estate
CRE non-owner-occupied
55% and below$100,859 $238,182 $190,263 $156,791 $192,044 $602,788 $11,031 — $1,491,958 
>55-65%71,824 220,089 110,830 136,117 84,644 217,812 559 — 841,875 
>65-75%18,018 105,825 171,532 18,946 13,796 30,887 — — 359,004 
Greater than 75%— 2,759 4,779 2,771 212 4,572 — — 15,093 
Multifamily
55% and below147,694 347,848 303,801 252,662 90,318 294,211 574 — 1,437,108 
>55-65%233,564 742,570 406,271 234,044 169,033 242,603 — — 2,028,085 
>65-75%257,091 628,393 244,007 262,512 153,542 96,542 — — 1,642,087 
Greater than 75%— 16,424 10,661 1,889 — 5,815 — — 34,789 
Construction and land
55% and below24,116 129,160 66,830 26,261 19,932 7,997 374 — 274,670 
>55-65%1,124 13,254 23,699 6,762 — — — — 44,839 
>65-75%— 3,295 15,068 — — — — — 18,363 
Greater than 75%— — — — — — — — — 
SBA secured by real estate
55% and below— 2,070 653 673 330 785 — — 4,511 
>55-65%— 2,433 1,643 4,017 621 4,482 — — 13,196 
>65-75%— 3,905 5,075 4,185 4,795 1,897 — — 19,857 
Greater than 75%494 2,167 5,804 6,665 1,365 3,551 — — 20,046 
Total investor loans secured by real estate$854,784 $2,458,374 $1,560,916 $1,114,295 $730,632 $1,513,942 $12,538 $— $8,245,481 
Business loan secured by real estate
CRE owner-occupied
55% and below$56,954 $156,880 $171,740 $203,875 $133,740 $357,734 $4,843 — $1,085,766 
>55-65%56,139 93,895 97,245 94,359 73,389 80,933 — — 495,960 
>65-75%55,932 155,872 80,898 46,241 32,547 50,458 — — 421,948 
Greater than 75%41,637 21,366 11,910 13,424 2,216 25,561 — — 116,114 
Franchise real estate secured
55% and below7,462 13,322 14,407 21,162 11,592 20,549 — — 88,494 
>55-65%— 9,981 15,893 23,658 7,784 5,862 — — 63,178 
>65-75%3,972 53,584 21,750 9,768 11,017 14,697 — — 114,788 
Greater than 75%9,073 10,862 22,409 48,168 1,119 1,238 — — 92,869 
SBA secured by real estate
55% and below1,355 1,633 5,376 5,683 3,175 15,281 95 — 32,598 
>55-65%104 513 1,802 1,719 3,706 5,665 — — 13,509 
>65-75%264 3,148 751 4,193 2,340 5,401 — — 16,097 
Greater than 75%920 2,364 6,147 7,509 1,336 3,646 — — 21,922 
Total business loans secured by real estate$233,812 $523,420 $450,328 $479,759 $283,961 $587,025 $4,938 $— $2,563,243 
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
September 30, 2020(Dollars in thousands)
Retail Loans
Single family residential
Greater than 740$4,469 $7,198 $12,719 $9,658 $29,291 $91,460 $22,566 — $177,361 
>680 - 740— 1,187 2,253 4,763 2,641 28,625 7,919 — 47,388 
>580 - 680— — — 463 3,142 11,113 932 — 15,650 
Less than 580— — — — — 2,925 35 — 2,960 
Consumer loans
Greater than 74063 85 54 46 10 2,648 1,670 — 4,576 
>680 - 740— 40 37,991 — 480 1,665 — 40,182 
>580 - 680— 17 — — 144 59 — 222 
Less than 580— — — — — 26 28 — 54 
Total retail loans$4,532 $8,527 $15,032 $52,921 $35,086 $137,421 $34,874 $— $288,393