Parkside Round Rock Resident Analysis

Who lives at Parkside at Round Rock? Show median household income and its 12-month change with the metro percentile, the income distribution, rent-to-income ratio, household age, and mobility score. Can these residents absorb the rent level the sponsor is underwriting, or is the problem somewhere else?

6:06 PM

Pulling the subject's tenant, rent, and financial data alongside the submarket benchmark.

Command

46 lines

mkdir -p analysis && cat > analysis/parkside_tenant_capacity.py <<'EOF'
import numpy as np
edges=[0,10e3,15e3,25e3,35e3,50e3,75e3,100e3,150e3,200e3,350e3,500e3,750e3]
subj=[0,0,0,0,0,.202247,.382022,.146067,.134831,.134831,0,0]
sub=[0,0,.000054,.004938,.024313,.139250,.164341,.250429,.054557,.175236,.146576,.040307]
def pct(sh,p):
    sh=np.array(sh)/sum(sh); c=0
    for i,s in enumerate(sh):
        if c+s>=p and s>0: return edges[i]+(p-c)/s*(edges[i+1]-edges[i])
        c+=s
print("Income percentiles (bracket-interpolated):")
for name,d in [("Subject",subj),("Submarket",sub)]:
    print(f"  {name}: P25 ${pct(d,.25):,.0f}  P50 ${pct(d,.5):,.0f}  P75 ${pct(d,.75):,.0f}  share<$75K {sum(d[:6])*100:.1f}%  share<$100K {sum(d[:7])*100:.1f}%")
rep_med_subj=119046; rep_med_sub=156894
print(f"Reported median subject ${rep_med_subj:,} vs submarket ${rep_med_sub:,}: {(rep_med_subj/rep_med_sub-1)*100:.1f}%")
b50=pct(subj,.5)
print(f"Bracket-implied subject median vs submarket reported: {(b50/rep_med_sub-1)*100:.1f}%")
ip_avg=1275.08; ip_med=1208; ask_avg=1318.58; sub_ip_avg=1445.50
print("\nRent-to-income (annual rent / income):")
for lbl,inc in [("reported median $119,046",rep_med_subj),(f"bracket median ${b50:,.0f}",b50),("P25 (bracket)",pct(subj,.25)),("$62.5K (mid of bottom bracket)",62500),("$50K (floor)",50000)]:
    print(f"  {lbl}: in-place avg {ip_avg*12/inc*100:.1f}% | asking avg {ask_avg*12/inc*100:.1f}%")
print(f"  Submarket: in-place avg ${sub_ip_avg} / median ${rep_med_sub:,} = {sub_ip_avg*12/rep_med_sub*100:.1f}%")
print("\nRent at 30% of income (monthly):")
for lbl,inc in [("reported median",rep_med_subj),("bracket median",b50),("P25",pct(subj,.25)),("$62.5K",62500),("$50K",50000)]:
    print(f"  {lbl}: ${inc*.30/12:,.0f}  headroom vs in-place avg {(inc*.30/12/ip_avg-1)*100:.0f}%")
# unit-type rent at bottom cohort
for bt,r in [("Studio",1021.85),("1BR",1190.75),("2BR",1576.71),("3BR",2311.00)]:
    print(f"  {bt} in-place ${r:,.0f}: RTI at $62.5K {r*12/62500*100:.1f}%, at bracket median {r*12/b50*100:.1f}%")
print("\nPricing vs submarket:")
print(f"  Asking avg: ${ask_avg} vs ${1507.18} -> {(ask_avg/1507.18-1)*100:.1f}%")
print(f"  Asking $/sf median: 1.72 vs 1.56 -> {(1.72/1.56-1)*100:.1f}%")
print(f"  In-place $/sf avg: 1.59 vs 1.56 -> {(1.59/1.56-1)*100:.1f}%")
for bt,a,s in [("0BR",1165.40,1205.80),("1BR",1245.37,1275.51),("2BR",1592.60,1663.09),("3BR",2691.00,1960.12)]:
    print(f"  {bt} asking ${a:,.0f} vs ${s:,.0f}: {(a/s-1)*100:+.1f}%")
for bt,a,s in [("3BR in-place",2311.00,1931.91)]:
    print(f"  {bt} ${a:,.0f} vs ${s:,.0f}: {(a/s-1)*100:+.1f}%")
print("\nOperations:")
print(f"  Occupancy 12mo ago 99.77% -> now 93.52%: {(0.9352-0.9977)*100:.1f} pts")
print(f"  Unleased 38 / 432 units = {38/432*100:.1f}%")
print(f"  Retention 67.4% vs submarket 63.1%: {(0.6736-0.631)*100:+.1f} pts")
print(f"  DOM 99 vs 66: {(99/66-1)*100:+.0f}%")
print(f"  Leases signed 13 in 30d vs 38 unleased -> months to clear {38/13:.1f}")
print(f"  NOI $3,086,585 / 2021 price $77.5M = {3086585.35/77.5e6*100:.2f}% yield on 2021 basis")
print(f"  Tax share of opex: {1439420.8/4128835.65*100:.1f}%")
EOF
python3 analysis/parkside_tenant_capacity.py

Parkside's renters can afford today's rents with plenty of room to spare. The weak spot is leasing: the property is losing occupancy and cutting rents in a submarket that is adding a lot of supply. I don't have the sponsor's underwritten rent, so I can't test that exact number. What the data shows is that rent affordability doesn't limit the deal except on the 3-bedrooms.

Who lives here

Parkside is a 432-unit, 5-story building built in 2019 with small units averaging 829 sf . Its renters are mostly young, single, middle-income professionals.

Metric	Parkside	Round Rock–East submarket	Read
Median household income	$119,046, 77th percentile in the Austin metro	$156,894	24.1% lower
Income change	+5.6% over 6 months, 88th metro percentile for growth	+8.4% over 12 months, 70th metro percentile	Growing faster than the metro
Rent-to-income	12.2%	11.1% (my calculation)	Very affordable: 7th percentile in the metro, where lower means more affordable
Head-of-household age	34	36.2	Younger
Single	79.8%	40.2%	Far more singles
Mobility score (lower = more likely to move soon)	2.9	32.1	Much more likely to move

Twelve-month income change for Parkside itself isn't available. The property's income time series came back empty, so the 6-month figure is the best property-level trend. The 12-month number above is for the submarket.

Treat the $119K median with caution. In the property's own income brackets, 58.4% of households earn under $100K, which puts the median closer to $94K . It's also a small sample: 106 households, about 21% of the building . Even using the lower median, rent-to-income is only 16.2% , so the conclusion holds either way.

Income Distribution: Parkside vs Submarket

The income distribution is tight. No household earns under $50K, 20% earn $50–75K, and 38% earn $75–100K . Wealth is thin, though. The median household has $25–50K in net worth and $10–15K in liquid savings . These renters handle the monthly rent comfortably but have little cushion for big increases.

Can they absorb higher rent?

Mostly yes. The limit only bites on the larger units:

Typical household: A household at the median can pay about $2,362 a month before rent reaches 30% of income. That is 85% above today's $1,275 average in-place rent . Even the 25th-percentile household has about 53% of room .
Lowest-income group ($50–75K): At the $62.5K midpoint, a 2BR in-place rent of $1,577 already takes 30.3% of income, and a 3BR at $2,311 takes 44.4% . This is where any rent growth assumption would hit a wall.
3-bedrooms are out of line with the market: Asking rent is $2,691, 37.3% above the submarket, and in-place rent is 19.6% above. Studios through 2-bedrooms ask 2–4% below the submarket .

The real problem: leasing momentum and supply

Occupancy has dropped. It fell from 99.8% to 93.5% over 12 months, a loss of 6.2 points, and 38 units are now unleased .
Rents are going backwards. Asking rent is down 4.75% and in-place rent is down 4.2% over the past year .
Units are slow to lease. They take a median 99 days to lease, versus 66 in the submarket. At 13 leases signed in the last 30 days, clearing the vacancy takes about 2.9 months .
The submarket is soft too. Housing supply grew 4.1% in the past year and 20.2% over five years . New leases in the submarket are signing about 1.6% below the prior tenant's rent .
Renewals are not the issue. Retention is 67.4%, about 4 points better than the submarket's 63.1% . Parkside's +12.2% new-lease trade-out is based on only a handful of recent leases and runs against every other rent signal, so I wouldn't rely on it.

Returns on the purchase price are also thin. Net operating income is $3.09M, which is a 3.98% yield on the $77.5M paid in 2021. Property taxes are 34.9% of operating expenses . That, more than rent levels, may be the actual underwriting problem.

Bottom line

If the sponsor's model shows rent growth that leaves the 3-bedrooms and occupancy as they are today, the renters can pay it; the property just can't lease units that fast. I'd push back on three assumptions:

Occupancy: closer to 93% than 97% while new supply is absorbed.
Near-term rent growth: flat for the first year.
3-bedroom rents: priced at or below today's in-place rent.

If you send the sponsor's rent assumptions by unit type, I'll test each one against these income brackets.