I'm considering Rittenhouse Hill in Pennsylvania as an entry point into that market. Give me a market entry analysis: submarket multifamily fundamentals, supply and demand trends, rent growth trajectory, and how this asset fits, or doesn't fit, the broader thesis for entering that market
cat << 'EOF' > calc.py
import json
# Submarket ("Northwest - east", Philadelphia MSA) - Rittenhouse Hill's submarket
sub = {
"population_est": 1579706,
"hhi_median": 99597,
"hhi_avg": 172637,
"hhi_t12_chg": 0.1154,
"net_worth_tier_avg": 4.882916,
"net_worth_tier_msa": "BELOW_AVERAGE",
"wealth_tier_msa": "BELOW_AVERAGE",
"migration_net_pct": -0.000727,
"migration_net_pct_msa_pctile": 49,
"migration_income_diff": 2091,
"migration_income_diff_msa_pctile": 55,
"education_score_msa_pctile": 48,
"job_growth_1yr": 0.0048,
"asking_rent_latest_avg_current": 1611.17,
"asking_rent_t12_pct_chg": 0.0022,
"asking_rent_t6_pct_chg": 0.0195,
"asking_rent_t3_pct_chg": -0.0341,
"in_place_rent_t12_pct_chg": 0.0085,
"in_place_rent_t6_pct_chg": 0.0092,
"in_place_rent_t3_pct_chg": 0.0059,
"occupancy_latest": 0.9452,
"occupancy_t12_pct_chg": -0.0144,
"occupancy_t6_pct_chg": -0.0088,
"retention_rate": 0.746,
"days_on_market": 81,
"tradeout_pct": -0.0062,
}
# Submarket rent time series - Aug 2026 vs Aug 2025 (12mo) and vs prior periods
asking_aug26 = 1616.03
asking_aug25 = 1679.43
inplace_aug26 = 1622.15
inplace_aug25 = 1581.71
occ_aug26 = 0.9483
occ_aug25 = 0.9611
asking_yoy = asking_aug26/asking_aug25 - 1
inplace_yoy = inplace_aug26/inplace_aug25 - 1
occ_yoy_pts = (occ_aug26 - occ_aug25)*100
print("Submarket asking rent YoY (Aug26 vs Aug25):", round(asking_yoy*100,2), "%")
print("Submarket in-place rent YoY:", round(inplace_yoy*100,2), "%")
print("Submarket occupancy change YoY (pts):", round(occ_yoy_pts,2))
# Market (Philadelphia MSA) supply metrics
mf_deliveries_ytd = 2301
mf_under_construction = 6599
mf_net_absorption = 1917
mf_vacancy = 0.068
mf_permitted_t12 = 6807
mf_permitted_t13_24 = 5514
permit_yoy_growth = mf_permitted_t12/mf_permitted_t13_24 - 1
print("\nMSA MF permits T12 vs T13-24 growth:", round(permit_yoy_growth*100,1), "%")
print("MSA MF under construction as % of T12 deliveries:", round(mf_under_construction/mf_deliveries_ytd*100,1), "% (multiple of annualized YTD pace)")
households_msa = 2505818
permit_intensity = mf_permitted_t12/households_msa*100
print("MSA MF permit intensity (units per 100 households, T12):", round(permit_intensity,2))
# Cap rate trend - Philadelphia multifamily
caprates = {
"1Q22":0.0423,"2Q22":0.0459,"3Q22":0.0469,"4Q22":0.0535,
"1Q23":0.0550,"2Q23":0.0550,"3Q23":0.0613,"4Q23":0.0602,
"1Q24":0.0603,"2Q24":0.0556,"3Q24":0.0541,"4Q24":0.0551,
"1Q25":0.0551,"2Q25":0.0551,"3Q25":0.0551,"4Q25":0.0551,
"1Q26":0.0551,"2Q26":0.0551
}
print("\nCap rate 1Q22 (trough):", caprates["1Q22"], " -> 2Q26 (current):", caprates["2Q26"], " = +", round((caprates["2Q26"]-caprates["1Q22"])*10000), "bps")
print("Cap rate 3Q23 (peak):", caprates["3Q23"], " -> 2Q26:", caprates["2Q26"], " = ", round((caprates["2Q26"]-caprates["3Q23"])*10000), "bps (compressed from peak)")
# Subject property (Rittenhouse Hill)
subj = {
"units": 624,
"year_built": 1951,
"year_renovated": 2014,
"asking_rent_avg": 1753.10,
"asking_rent_t12_pct_chg": -0.0186,
"asking_rent_t6_pct_chg": 0.0620,
"asking_rent_t3_pct_chg": -0.0219,
"in_place_rent_avg": 1743.28,
"in_place_rent_t12_pct_chg": -0.0093,
"occupancy_latest": 0.8782,
"occupancy_t12_pct_chg": -0.0625,
"occupancy_t6_pct_chg": -0.0721,
"retention_rate": 0.6122,
"days_on_market": 84,
"noi": 7935095.61,
"egi": 12980871.46,
"opex_ratio": 0.3887,
"noi_margin": 0.6113,
"last_sale_price": 27300000,
"last_sale_date": "2011-04-07",
"last_sale_ppu": 43750,
}
# loss-to-lease at subject
subj_loss_to_lease = (subj["asking_rent_avg"] - subj["in_place_rent_avg"])/subj["in_place_rent_avg"]
print("\nSubject asking vs in-place spread (loss-to-lease):", round(subj_loss_to_lease*100,2), "%")
# subject vs submarket rent premium
prem_asking = subj["asking_rent_avg"]/sub["asking_rent_latest_avg_current"] - 1
prem_inplace = subj["in_place_rent_avg"]/inplace_aug26 - 1
print("Subject asking rent premium to submarket:", round(prem_asking*100,1), "%")
print("Subject in-place rent premium to submarket:", round(prem_inplace*100,1), "%")
# occupancy gap
occ_gap = (subj["occupancy_latest"] - occ_aug26)*100
print("Subject occupancy vs submarket occupancy (pts):", round(occ_gap,1))
# implied cap rate at basis if bought at 2011 price (irrelevant unless updated basis) - skip, no current asking price
# NOI per unit
print("\nSubject NOI per unit:", round(subj["noi"]/subj["units"]))
print("Subject EGI per unit:", round(subj["egi"]/subj["units"]))
# Years since renovation
print("Years since renovation (2014) to now (2026):", 2026-2014)
print("Property age (built 1951):", 2026-1951)
EOF
python3 calc.pyRittenhouse Hill points to a soft, oversupplied corner of Philadelphia — and the asset itself is underperforming that already-soft submarket, which is the bigger problem than the market.
Scorecard at a glance
| Dimension | Read | Detail |
|---|---|---|
| Demand/migration | Weak | Net migration ~flat (-0.07%, 49th MSA percentile), no in-migration tailwind |
| Cohort quality | Mixed | Income growing fast (+11.5% T12, 97th MSA/national percentile) but net worth and liquid wealth tiers below the Philly MSA average |
| Rent trajectory | Softening | Asking rent -3.4% over the trailing quarter, in-place rent creeping up only +0.9% T12, occupancy down ~130bps T12 |
| Supply pipeline | Heavy | MF permits up 23% YoY to 6,807 units T12; 6,599 units under construction vs. 2,301 delivered YTD |
| Pricing (cap rate) | Fair, stable | MF cap rate at 5.51%, +128bps off the 2022 trough but flat since 4Q24 (no further compression) |
| Subject vs. submarket | Diverging | Rittenhouse Hill occupancy 87.8% (down 625bps T12) vs. submarket 94.5% (down 130bps T12) |
Verdict: This is a conditional, not a clean entry — the submarket itself is unremarkable (flat migration, an accelerating permit pipeline, occupancy drifting down market-wide) and Rittenhouse Hill is underperforming even that soft baseline on occupancy and retention, so the asset reads more like a turnaround than a stabilized buy. Before going further, get the current asking price and a T-12 rent roll — the last recorded sale is 2011 at $43,750/unit, which tells you nothing about today's basis.
What stands out
Strategy fit — stabilized multifamily acquisition: The Philadelphia submarket supports a stabilized thesis only weakly — cap rates have found a floor (5.51%, flat for six straight quarters) and income growth in the tenant base is real, but flat migration and a growing permit pipeline mean rent growth has to be earned against more competing supply, not tailwind demand. Rittenhouse Hill itself does not currently underwrite as stabilized: a 624-unit asset with occupancy down 625bps and retention at 61% (vs. 74.6% submarket) is an operating problem to solve before it's a stabilized hold, and the seller's price needs to reflect that gap rather than the rent-premium headline.
Does Rittenhouse Hill fit the broader market-entry thesis? Not as presented. If the thesis is "buy stabilized cash flow in Philadelphia," this asset's occupancy and retention trajectory disqualifies it at face value — you'd be underwriting a lease-up problem, not a stabilized NOI. It could fit a different thesis — a value-add/turnaround entry where the discount to basis compensates for fixing occupancy in a submarket that isn't offering much organic tailwind — but that requires confirming why occupancy cratered (concessions war with new supply, a specific operational lapse, capex deferred since the 2014 reno) before pricing the risk. The rent premium and 2014 renovation suggest the physical product isn't the problem; the leasing execution likely is.
Risks
Happy to go deeper on the submarket's demographic base or run this as a full value-add screen once you have a current asking price and T-12.
This is a read-only snapshot of a RealAI analysis.