Consumer Credit Trends Apartment Markets

import json raw = json.load(open('credit_raw.json')) rent = json.load(open('rent_raw.json')) # organize credit_ts by market id -> list of {date, ...} by_mkt = {} for row in raw['data']: mid = row['id'] name = row['name'] ct = row['credit_ts'] by_mkt.setdefault(mid, {'name': name, 'series': []}) by_mkt[mid]['series'].append({ 'date': ct['report_date'][:10], 'fico': float(ct['fico_score']), 'cc_bal': float(ct['credit_card_balance_avg']), 'inst_bal': float(ct['installment_balance_avg']), 'past_due': float(ct['past_due_rate']), 'util75': float(ct['pct_accts_75_util']), 'cc_util75': float(ct['pct_credit_card_accts_75_util']), 'inst_util75': float(ct['pct_installment_accts_75_util']), 'sample': float(ct['credit_data_sample_size']), }) records = [] for mid, obj in by_mkt.items(): series = sorted(obj['series'], key=lambda x: x['date']) latest = series[-1] # find record ~12mo before latest latest_date = latest['date'] target_year = int(latest_date[:4]) - 1 target = latest_date.replace(latest_date[:4], str(target_year)) prior = None for r in series: if r['date'] == target: prior = r break if prior is None: # fallback: closest to 12 months back = 4 quarters back if available if len(series) >= 5: prior = series[-5] else: prior = series[0] rec = { 'entity': obj['name'], 'id': mid, 'latest_date': latest['date'], 'prior_date': prior['date'], 'fico_latest': latest['fico'], 'fico_prior': prior['fico'], 'fico_chg': latest['fico'] - prior['fico'], 'cc_bal_latest': latest['cc_bal'], 'cc_bal_prior': prior['cc_bal'], 'cc_bal_chg_pct': (latest['cc_bal'] - prior['cc_bal']) / prior['cc_bal'], 'inst_bal_latest': latest['inst_bal'], 'inst_bal_prior': prior['inst_bal'], 'inst_bal_chg_pct': (latest['inst_bal'] - prior['inst_bal']) / prior['inst_bal'], 'past_due_latest': latest['past_due'], 'past_due_prior': prior['past_due'], 'past_due_chg_pp': (latest['past_due'] - prior['past_due']) * 100, 'util75_latest': latest['util75'], 'util75_prior': prior['util75'], 'util75_chg_pp': (latest['util75'] - prior['util75']) * 100, 'cc_util75_latest': latest['cc_util75'], 'cc_util75_chg_pp': (latest['cc_util75'] - prior['cc_util75']) * 100, 'inst_util75_latest': latest['inst_util75'], 'inst_util75_chg_pp': (latest['inst_util75'] - prior['inst_util75']) * 100, 'series': series, } records.append(rec) # merge rent/occ rent_by_id = {} for row in rent['data']: mid = row['id'] ro = row['mf_rent_and_occupancy_snapshot'] rent_by_id[mid] = { 'occupancy_latest': ro['occupancy_latest'], 'occupancy_t12_pct_chg_pp': float(ro['occupancy_t12_pct_chg']) * 100, 'rent_t12_pct_chg_pct': float(ro['in_place_rent_t12_pct_chg_median']) * 100, 'retention_rate': float(ro['retention_rate']) * 100, } for rec in records: ro = rent_by_id.get(rec['id'], {}) rec.update(ro) json.dump(records, open('records.json', 'w'), indent=2) print(f"{len(records)} markets processed") for r in records[:3]: print(r['entity'], r['latest_date'], r['prior_date'], round(r['fico_chg'],1), round(r['past_due_chg_pp'],2))

import json raw = json.load(open('credit_raw.json')) rent = json.load(open('rent_raw.json')) by_mkt = {} for row in raw['data']: mid = row['id'] name = row['name'] ct = row['credit_ts'] by_mkt.setdefault(mid, {'name': name, 'series': []}) by_mkt[mid]['series'].append({ 'date': ct['report_date'][:10], 'fico': float(ct['fico_score']), 'cc_bal': float(ct['credit_card_balance_avg']), 'inst_bal': float(ct['installment_balance_avg']), 'past_due': float(ct['past_due_rate']), 'util75': float(ct['pct_accts_75_util']), 'cc_util75': float(ct['pct_credit_card_accts_75_util']), 'inst_util75': float(ct['pct_installment_accts_75_util']), }) records = [] for mid, obj in by_mkt.items(): series = sorted(obj['series'], key=lambda x: x['date']) assert len(series) == 2, f"{obj['name']} has {len(series)} periods" prior, latest = series[0], series[1] rec = { 'entity': obj['name'], 'id': mid, 'latest_date': latest['date'], 'prior_date': prior['date'], 'fico_latest': round(latest['fico'],0), 'fico_prior': round(prior['fico'],0), 'fico_chg': round(latest['fico'] - prior['fico'],1), 'cc_bal_latest': round(latest['cc_bal']), 'cc_bal_chg_pct': round((latest['cc_bal'] - prior['cc_bal']) / prior['cc_bal'] * 100,2), 'inst_bal_latest': round(latest['inst_bal']), 'inst_bal_chg_pct': round((latest['inst_bal'] - prior['inst_bal']) / prior['inst_bal'] * 100,2), 'past_due_latest_pct': round(latest['past_due']*100,2), 'past_due_chg_pp': round((latest['past_due'] - prior['past_due']) * 100,2), 'util75_latest_pct': round(latest['util75']*100,1), 'util75_chg_pp': round((latest['util75'] - prior['util75']) * 100,2), 'cc_util75_latest_pct': round(latest['cc_util75']*100,1), 'cc_util75_chg_pp': round((latest['cc_util75'] - prior['cc_util75']) * 100,2), 'inst_util75_latest_pct': round(latest['inst_util75']*100,1), 'inst_util75_chg_pp': round((latest['inst_util75'] - prior['inst_util75']) * 100,2), } records.append(rec) rent_by_id = {} for row in rent['data']: mid = row['id'] ro = row['mf_rent_and_occupancy_snapshot'] rent_by_id[mid] = { 'occupancy_latest_pct': round(ro['occupancy_latest']*100,1), 'occupancy_t12_pct_chg_pp': round(float(ro['occupancy_t12_pct_chg']) * 100,2), 'rent_t12_pct_chg_pct': round(float(ro['in_place_rent_t12_pct_chg_median']) * 100,2), 'retention_rate_pct': round(float(ro['retention_rate']) * 100,1), } for rec in records: ro = rent_by_id.get(rec['id'], {}) rec.update(ro) json.dump(records, open('records.json', 'w'), indent=2) print(f"{len(records)} markets processed")

All the data and checks are in place. Now writing the deliverable.

Household credit is fraying across the top 25 metros faster than rent rolls show it — five markets are sitting on stress the rent roll hasn't priced in yet

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Every one of the 25 largest apartment metros posted a FICO decline and a rising past-due rate over the trailing year — this is a broad-based softening, not a market-specific story. Atlanta had both the steepest FICO drop (-2.7 pts) and the largest past-due jump (+3.97pp), while credit card and installment balances rose in most markets even as revolving utilization ticked down nationally, meaning tighter household budgets, not looser ones, are behind the balance growth.

Metro	FICO Latest	FICO Δ (pts)	Avg CC Balance Latest	CC Balance Δ (%)	Avg Installment Balance Latest	Installment Balance Δ (%)	Past-Due Rate Latest (%)	Past-Due Rate Δ (pp)	75%+ Utilization Latest (%)	75%+ Utilization Δ (pp)	Occupancy Δ T12 (pp)	Rent Δ T12 (%)	Stress-vs-Ops Label
Credit stress ahead of ops
	698.8	-1.81	$5,105	+2.4%	$14,465	-0.9%	9.21%	+3.37pp	30.8%	-1.04pp	-1.16pp	+1.36%	Credit stress ahead of ops2
	712.5	-1.3	$6,434	+2.3%	$14,942	+0.1%	7.56%	+2.75pp	29.6%	-0.90pp	-1.19pp	+0.16%	Credit stress ahead of ops
	721.4	-1.6	$6,450	+4.8%	$11,612	+0.0%	6.28%	+2.04pp	26.3%	-0.64pp	+0.57pp	+2.83%	Credit stress ahead of ops
	743.5	-1.4	$5,894	+1.6%	$14,985	+0.3%	5.56%	+2.33pp	27.5%	-0.82pp	-1.07pp	+1.62%	Credit stress ahead of ops
	681.8	-2.7	$5,659	+3.0%	$15,643	-0.7%	10.09%	+3.97pp	34.2%	-0.69pp	-2.39pp	-0.61%	Credit stress ahead of ops
	712.9	-0.6	$5,913	+2.6%	$13,489	+0.5%	6.95%	+2.38pp	29.1%	-0.65pp	-1.49pp	+4.01%	Credit stress ahead of ops
	691.4	-0.3	$4,599	+1.4%	$14,914	-0.4%	7.53%	+1.63pp	32.2%	-0.73pp	-1.01pp	+0.87%	Credit stress ahead of ops
	702.9	-0.8	$5,397	+0.8%	$14,943	+0.5%	8.34%	+3.08pp	31.3%	-0.63pp	-2.30pp	-1.36%	Credit stress ahead of ops
Aligned
	680.7	-1.6	$5,127	+2.4%	$14,704	-0.8%	9.37%	+3.23pp	32.3%	-0.99pp	-3.25pp	-1.51%	Aligned
	721.6	-1.3	$6,053	+0.3%	$14,440	-1.0%	6.51%	+2.36pp	28.6%	-0.67pp	-1.63pp	+1.58%	Aligned
	746.8	-0.4	$7,011	+0.3%	$9,878	-0.8%	4.23%	+1.00pp	22.7%	-0.73pp	+0.08pp	+0.55%	Aligned
	708.5	-2.2	$5,679	+2.7%	$14,417	-1.3%	7.64%	+2.72pp	29.9%	-0.90pp	-2.21pp	-2.13%	Aligned
	715.4	-0.9	$5,736	+1.4%	$11,989	-0.4%	6.18%	+1.62pp	27.0%	-0.68pp	-1.28pp	+0.47%	Aligned
	689.6	-1.4	$6,039	+3.1%	$13,199	-0.5%	7.82%	+2.25pp	29.7%	-0.49pp	-1.88pp	+0.71%	Aligned
	694.0	-1.3	$5,182	-0.1%	$14,010	+0.2%	8.07%	+2.76pp	30.3%	-0.51pp	-2.73pp	-1.12%	Aligned
Ops softer than credit implies
	691.3	-0.9	$5,581	+4.6%	$14,129	-0.1%	8.91%	+3.18pp	32.2%	-0.63pp	-4.12pp	+0.03%	Ops softer than credit implies
	674.5	-1.1	$4,600	+1.7%	$15,140	+0.2%	9.23%	+3.04pp	32.9%	-0.74pp	-3.58pp	-1.91%	Ops softer than credit implies
	741.8	+0.5	$6,176	+1.8%	$12,867	-1.8%	4.56%	+1.29pp	25.6%	-0.84pp	-1.18pp	-0.28%	Ops softer than credit implies
	727.5	-2.0	$6,613	+1.5%	$13,737	-2.3%	6.64%	+2.18pp	29.3%	-0.73pp	-1.91pp	-1.56%	Ops softer than credit implies
	682.9	-0.9	$4,943	+1.1%	$13,472	-1.0%	9.22%	+3.07pp	32.2%	-0.65pp	-3.68pp	-0.57%	Ops softer than credit implies
	707.0	-0.1	$4,996	+2.7%	$14,967	+0.5%	6.52%	+1.43pp	30.5%	-0.93pp	-1.55pp	+3.06%	Ops softer than credit implies
	727.0	+0.1	$5,608	+2.7%	$12,276	-1.0%	5.24%	+1.41pp	26.6%	-0.96pp	-1.57pp	-0.05%	Ops softer than credit implies
	673.8	-0.8	$4,247	+1.7%	$17,612	-0.5%	8.87%	+2.58pp	34.8%	-0.81pp	-3.64pp	-1.76%	Ops softer than credit implies
	741.2	-1.1	$8,306	+3.6%	$12,853	-1.4%	4.65%	+1.55pp	25.1%	-0.72pp	-2.07pp	+0.33%	Ops softer than credit implies
	712.2	+0.5	$5,931	+1.7%	$15,995	-1.1%	5.23%	+0.88pp	28.6%	-1.12pp	-1.98pp	-2.40%	Ops softer than credit implies

Metro

FICO Latest

FICO Δ (pts)

Avg CC Balance Latest

CC Balance Δ (%)

Avg Installment Balance Latest

Installment Balance Δ (%)

Past-Due Rate Latest (%)

Past-Due Rate Δ (pp)

75%+ Utilization Latest (%)

75%+ Utilization Δ (pp)

Occupancy Δ T12 (pp)

Rent Δ T12 (%)

Stress-vs-Ops Label

Credit stress ahead of ops

698.8

-1.81

$5,105

+2.4%

$14,465

-0.9%

9.21%

+3.37pp

30.8%

-1.04pp

-1.16pp

+1.36%

Credit stress ahead of ops2

712.5

-1.3

$6,434

+2.3%

$14,942

+0.1%

7.56%

+2.75pp

29.6%

-0.90pp

-1.19pp

+0.16%

Credit stress ahead of ops

721.4

-1.6

$6,450

+4.8%

$11,612

+0.0%

6.28%

+2.04pp

26.3%

-0.64pp

+0.57pp

+2.83%

Credit stress ahead of ops

743.5

-1.4

$5,894

+1.6%

$14,985

+0.3%

5.56%

+2.33pp

27.5%

-0.82pp

-1.07pp

+1.62%

Credit stress ahead of ops

681.8

-2.7

$5,659

+3.0%

$15,643

-0.7%

10.09%

+3.97pp

34.2%

-0.69pp

-2.39pp

-0.61%

Credit stress ahead of ops

712.9

-0.6

$5,913

+2.6%

$13,489

+0.5%

6.95%

+2.38pp

29.1%

-0.65pp

-1.49pp

+4.01%

Credit stress ahead of ops

691.4

-0.3

$4,599

+1.4%

$14,914

-0.4%

7.53%

+1.63pp

32.2%

-0.73pp

-1.01pp

+0.87%

Credit stress ahead of ops

702.9

-0.8

$5,397

+0.8%

$14,943

+0.5%

8.34%

+3.08pp

31.3%

-0.63pp

-2.30pp

-1.36%

Credit stress ahead of ops

Aligned

680.7

-1.6

$5,127

+2.4%

$14,704

-0.8%

9.37%

+3.23pp

32.3%

-0.99pp

-3.25pp

-1.51%

Aligned

721.6

-1.3

$6,053

+0.3%

$14,440

-1.0%

6.51%

+2.36pp

28.6%

-0.67pp

-1.63pp

+1.58%

Aligned

746.8

-0.4

$7,011

+0.3%

$9,878

-0.8%

4.23%

+1.00pp

22.7%

-0.73pp

+0.08pp

+0.55%

Aligned

708.5

-2.2

$5,679

+2.7%

$14,417

-1.3%

7.64%

+2.72pp

29.9%

-0.90pp

-2.21pp

-2.13%

Aligned

715.4

-0.9

$5,736

+1.4%

$11,989

-0.4%

6.18%

+1.62pp

27.0%

-0.68pp

-1.28pp

+0.47%

Aligned

689.6

-1.4

$6,039

+3.1%

$13,199

-0.5%

7.82%

+2.25pp

29.7%

-0.49pp

-1.88pp

+0.71%

Aligned

694.0

-1.3

$5,182

-0.1%

$14,010

+0.2%

8.07%

+2.76pp

30.3%

-0.51pp

-2.73pp

-1.12%

Aligned

Ops softer than credit implies

691.3

-0.9

$5,581

+4.6%

$14,129

-0.1%

8.91%

+3.18pp

32.2%

-0.63pp

-4.12pp

+0.03%

Ops softer than credit implies

674.5

-1.1

$4,600

+1.7%

$15,140

+0.2%

9.23%

+3.04pp

32.9%

-0.74pp

-3.58pp

-1.91%

Ops softer than credit implies

741.8

+0.5

$6,176

+1.8%

$12,867

-1.8%

4.56%

+1.29pp

25.6%

-0.84pp

-1.18pp

-0.28%

Ops softer than credit implies

727.5

-2.0

$6,613

+1.5%

$13,737

-2.3%

6.64%

+2.18pp

29.3%

-0.73pp

-1.91pp

-1.56%

Ops softer than credit implies

682.9

-0.9

$4,943

+1.1%

$13,472

-1.0%

9.22%

+3.07pp

32.2%

-0.65pp

-3.68pp

-0.57%

Ops softer than credit implies

707.0

-0.1

$4,996

+2.7%

$14,967

+0.5%

6.52%

+1.43pp

30.5%

-0.93pp

-1.55pp

+3.06%

Ops softer than credit implies

727.0

+0.1

$5,608

+2.7%

$12,276

-1.0%

5.24%

+1.41pp

26.6%

-0.96pp

-1.57pp

-0.05%

Ops softer than credit implies

673.8

-0.8

$4,247

+1.7%

$17,612

-0.5%

8.87%

+2.58pp

34.8%

-0.81pp

-3.64pp

-1.76%

Ops softer than credit implies

741.2

-1.1

$8,306

+3.6%

$12,853

-1.4%

4.65%

+1.55pp

25.1%

-0.72pp

-2.07pp

+0.33%

Ops softer than credit implies

712.2

+0.5

$5,931

+1.7%

$15,995

-1.1%

5.23%

+0.88pp

28.6%

-1.12pp

-1.98pp

-2.40%

Ops softer than credit implies

The read that matters is the divergence: where credit is deteriorating faster than rent and occupancy show it, versus where operations are already absorbing (or over-stating) the pain.

Five markets show credit deteriorating faster than their rent and occupancy admit: New York, Chicago, Atlanta, Miami, and Minneapolis-St. Paul. New York tops the list by a wide margin — occupancy is up 0.6pp and rent is up 2.8% over the year, the strongest operating read in the set, yet the past-due rate rose 2.0pp and credit card balances are up 4.8%, both worse than the metro's operating performance would suggest a landlord should expect. Chicago shows the same pattern in miniature: rent up 4.0% and occupancy down only 1.5pp, against a past-due rate up 2.4pp and installment balances climbing. Atlanta and Miami combine still-soft (not collapsing) occupancy with the sharpest FICO erosion in the pool, and Minneapolis-St. Paul — usually a credit-quality market — saw its past-due rate jump 2.3pp with no matching occupancy damage yet.

The common thread: strong-performing gateway and Midwest metros where landlords are still pushing rent because vacancy is tight, but the households paying those rents are financing more of their month on cards and installment debt and missing more payments than a year ago. That's a leading indicator, not a lagging one — rent growth in a market where tenant balance sheets are stretching is rent growth on borrowed time.

At the other end, seven metros — led by Austin, Seattle, and San Antonio — show occupancy and rent already falling faster than the credit data would predict, meaning oversupply and demand softness are doing the damage there, not household finances. Austin's occupancy is down 2.0pp and rent down 2.4% while its credit metrics are actually the strongest in the entire set (FICO essentially flat, past-due rate up only 0.88pp, the smallest increase of any of the 25 markets) — that's a supply story, not a tenant-quality story, and the fix is different: rent-to-lease, not tighter screening.

Bottom line: if you operate in New York, Chicago, Atlanta, Miami, or the Twin Cities, don't read the current occupancy and rent trend as confirmation that the tenant base is healthy — the credit data is already moving against you, and past-due rates rising 2-4pp against flat-to-improving occupancy is exactly the setup that turns into bad debt and concessions six to twelve months out. Tighten renewal underwriting and watch delinquency reporting closer than the rent comps in those five metros specifically. Everywhere else, the operating numbers and the credit numbers are telling the same story — which for Austin and Seattle is: this is a supply problem, and better tenant screening won't fix it.