Main Street Towns Lilburn Rent Comp Analysis

Evaluate the unit pricing at Main Street Towns at Lilburn against comparable rent comps in the surrounding market, including how in-place and asking rents compare by bedroom type. Use my Yardi data and pricing history. Select comps based on distance, unit mix overlap, unit count, building style, and vintage or quality, then flag whether our pricing is both competitive and maximizes long term revenue. give 1–3 actionable pricing recommendations if applicable.

7:42 PM

Now Yardi subject data: unit mix, market rent, occupancy, in-place rent.

Command

36 lines

mkdir -p extracts scripts && cp tool-outputs/comps_lilburn_place.json tool-outputs/comps_gwinnett_county.json extracts/ && cat > scripts/comp_pool.py <<'EOF'
import json, math
SUBJ_ID="dea1bb1fdba58ff614840cbff071ea63"; LAT,LON=33.89249771833428,-84.14109528064728
rows={}
for f in ["extracts/comps_lilburn_place.json","extracts/comps_gwinnett_county.json"]:
    d=json.load(open(f)); d=d.get("data",d) if isinstance(d,dict) else d
    for r in d: rows[r["id"]]=r
rows.pop(SUBJ_ID,None)
def hv(lat,lon):
    R=3958.8;p1,p2=math.radians(LAT),math.radians(lat);dp=p2-p1;dl=math.radians(lon-LON)
    a=math.sin(dp/2)**2+math.cos(p1)*math.cos(p2)*math.sin(dl/2)**2;return 2*R*math.asin(math.sqrt(a))
out=[]
for r in rows.values():
    a=r["mf_property_attributes"];s=r["mf_rent_and_occupancy_snapshot"]
    hr=a.get("household_restrictions"); restr="none"
    if hr:
        try:
            j=json.loads(hr); act=[k for k,v in j.items() if v]; restr=",".join(act) if act else "none"
        except: restr="unknown"
    f=lambda k: float(s[k]) if s.get(k) not in (None,"") else None
    out.append(dict(id=r["id"],name=r["name"],city=r["address_city"],dist=round(hv(a["latitude"],a["longitude"]),2),units=a["unit_count"],style=a["building_style"],yb=a["year_built"],yr=a.get("year_renovated"),sqft=a.get("unit_size_sqft"),restr=restr,
      ask3=f("asking_rent_latest_3_bed"),ip3=f("in_place_rent_latest_3_bed"),ask3psf=f("asking_rent_sqft_latest_3_bed"),ip3psf=f("in_place_rent_sqft_latest_3_bed"),
      ask2=f("asking_rent_latest_2_bed"),ip2=f("in_place_rent_latest_2_bed"),ask4=f("asking_rent_latest_4_bed"),ip4=f("in_place_rent_latest_4_bed"),
      ask_avg=f("asking_rent_latest_avg"),ip_avg=f("in_place_rent_latest_avg"),occ=f("occupancy_latest"),ret=s.get("retention_rate"),dom=s.get("days_on_market_leases_signed_past_30d_median"),
      ask_t12=f("asking_rent_t12_pct_chg_median"),ip_t12=f("in_place_rent_t12_pct_chg_median"),conf=s.get("sample_confidence"),asof=s.get("latest_as_of")))
out.sort(key=lambda x:x["dist"])
json.dump(out,open("extracts/comp_pool_merged.json","w"),indent=1)
print(len(out),"candidates; styles:",sorted(set(str(o["style"]) for o in out)))
print("within 5mi:",sum(o["dist"]<5 for o in out))
for o in out[:30]: print(o["dist"],o["name"][:32],o["units"],o["style"],o["yb"],o["yr"],o["restr"],o["sqft"],"ask3",o["ask3"],"ip3",o["ip3"],"occ",o["occ"])
print("--- TOWNHOUSE or 3BR-heavy newer anywhere in county ---")
for o in out:
    if (o["style"]=="TOWNHOUSE") or (o["yb"] and int(o["yb"])>=2015 and o["ip3"]):
        print(o["dist"],o["name"][:32],o["units"],o["style"],o["yb"],o["restr"],o["sqft"],"ask3",o["ask3"],"ip3",o["ip3"],"psf",o["ip3psf"],"occ",o["occ"])
EOF
python3 scripts/comp_pool.py

Command

46 lines

cat > scripts/comp_analysis.py <<'EOF'
import json, statistics as st
out={o["name"]:o for o in json.load(open("extracts/comp_pool_merged.json"))}
SEL=[("Parkview Place","Product"),("Monfort Reserve","Product"),("Generation Avondale East","Product"),("Residences on Paper Mill","Product"),("Carlton Crossing","Product"),("The Mabry","Product"),
     ("The Darnell","Nearby new"),("Lyra by Radius","Nearby new"),("Celadon on Club Apartments","Nearby new"),("District at Parkview","Nearby new")]
# Subject (Yardi = operator truth; Datamart for like-for-like)
S=dict(sqft=2138, yardi_market=2816, yardi_inplace_gross=2734, dm_ask3=2715, dm_ip3=2675.29, new_lease_net_eff=2671, occ_yardi=79/80)
rows=[]
for n,g in SEL:
    o=out[n]
    sf3=round(o["ask3"]/o["ask3psf"]) if o["ask3"] and o["ask3psf"] else (round(o["ip3"]/o["ip3psf"]) if o["ip3"] and o["ip3psf"] else None)
    rows.append(dict(name=n,group=g,dist=o["dist"],units=o["units"],style=o["style"],yb=o["yb"],sf3=sf3,ask3=o["ask3"],ip3=o["ip3"],
        ask3psf=o["ask3psf"],ip3psf=o["ip3psf"],occ=o["occ"],ret=o["ret"],ask_t12=o["ask_t12"],ip_t12=o["ip_t12"],dom=o["dom"],id=o["id"]))
for r in rows: print(r)
def stats(key,grp=None):
    v=[r[key] for r in rows if r[key] is not None and (grp is None or r["group"]==grp)]
    return dict(n=len(v),avg=round(st.mean(v),2),med=round(st.median(v),2),min=min(v),max=max(v))
res={}
for grp in [None,"Product","Nearby new"]:
    k=grp or "All"
    res[k]={key:stats(key,grp) for key in ["ask3","ip3","ask3psf","ip3psf","sf3","occ","ask_t12","ip_t12"]}
print(json.dumps(res,indent=1))
P=res["Product"]; A=res["All"]
subj_ask_psf=S["yardi_market"]/S["sqft"]; subj_ip_psf=S["yardi_inplace_gross"]/S["sqft"]
print("subject yardi ask psf",round(subj_ask_psf,2),"ip psf",round(subj_ip_psf,2), "dm ask psf", round(S["dm_ask3"]/S["sqft"],2),"dm ip psf",round(S["dm_ip3"]/S["sqft"],2))
for lab,v in [("yardi_market",S["yardi_market"]),("dm_ask3",S["dm_ask3"]),("yardi_inplace",S["yardi_inplace_gross"]),("dm_ip3",S["dm_ip3"]),("net_eff_new",S["new_lease_net_eff"])]:
    for grp in ["Product","All"]:
        base=res[grp]["ask3" if "ask" in lab or "market" in lab or "net" in lab else "ip3"]["avg"]
        print(lab,grp,"vs avg",round(v-base),f"{100*(v/base-1):.1f}%")
# ranks
ask_sorted=sorted([r["ask3"] for r in rows if r["ask3"]]+[S["yardi_market"]],reverse=True)
print("subject Yardi market rent rank among",len(ask_sorted),":",ask_sorted.index(S["yardi_market"])+1)
ip_sorted=sorted([r["ip3"] for r in rows if r["ip3"]]+[S["yardi_inplace_gross"]],reverse=True)
print("subject in-place rank among",len(ip_sorted),":",ip_sorted.index(S["yardi_inplace_gross"])+1)
# Parkview Place direct
pp=out["Parkview Place"]; print("vs Parkview ask",S["yardi_market"]-pp["ask3"],"ip",S["yardi_inplace_gross"]-pp["ip3"])
# Size-adjusted value: product-comp median psf * subject sqft
for key in ["ask3psf","ip3psf"]:
    print(key,"product median psf x subject sf =",round(P[key]["med"]*S["sqft"]),"| avg:",round(P[key]["avg"]*S["sqft"]))
# loss to lease
print("Yardi loss-to-lease $",S["yardi_market"]-S["yardi_inplace_gross"], f"{100*(1-S['yardi_inplace_gross']/S['yardi_market']):.1f}%")
# revenue sizing: $ per $25 / $50 increment
for inc in [25,50,75]: print("inc",inc,"annual @79 occ", inc*79*12)
json.dump(dict(rows=rows,stats=res,subject=S),open("extracts/comp_analysis_out.json","w"),indent=1)
EOF
python3 scripts/comp_analysis.py

Bottom line

Your pricing is competitive, and you have room to charge more. Main Street Townes posts the highest 3BR rent in the comp set and is still 98.8% occupied, while comps sit around 93% and are cutting asking rents. You aren't capturing all of that pricing power, though. Posted rent has barely moved since mid-2024, new leases have stopped gaining on the leases they replace, and you're still giving concessions at full occupancy.

One limit on "by bedroom type": all 80 units are 3BR/2.5BA townhomes averaging 2,138 sf , built in 2023 . So 3BR is the only bedroom type to compare.

Your rents vs. the comps

	Main Street Townes	Townhome/build-to-rent comps (6)	Nearby new apartments (4)
Asking rent	$2,816 (Yardi market rent)	avg $2,574	avg $2,528
In-place rent	$2,734 (before concessions)	avg $2,474	avg $2,524
Occupancy	79 of 80 units	~93%	~93%

Asking rent ranks #1 of 10 and in-place rent ranks #3 of 11 .
Closest true peer: Parkview Place is a 2024 build-to-rent community 4.8 miles away with 3BRs almost the same size (2,115 sf). You are $193 above it on asking rent and $245 above it on in-place rent .
Per square foot you look cheap ($1.32/sf vs. a ~$1.53 comp median). That's mostly because your units are larger, and rent per sf falls as size rises. Don't use it to justify a big jump. The dollar comparison is the better guide.

Rent Comps – Main Street Townes at Lilburn (3BR)

Property	Group	Distance (mi)	Units	Style	Year Built	3BR sf (implied)	3BR Asking	3BR In-Place	Asking $/sf	In-Place $/sf	Occupancy	T12 Asking Chg
	Subject	—	80	Townhouse	2023	2,138	$2,8161	$2,734	$1.32	$1.28	98.8%	—
Townhome / BTR product comps
	Product	4.76	147	Build-for-rent	2024	2,1152	$2,6233	$2,489	$1.24	$1.26	95.2%	-2.3%
	Product	7.20	95	Townhouse	2023	1,833	$2,713	$2,586	$1.48	$1.41	88.4%	+3.3%
	Product	10.18	239	Townhouse	2024	1,635	$2,796	$2,807	$1.71	$1.72	97.5%	-3.2%
	Product	10.76	116	Townhouse	2023	1,433	$2,264	$2,172	$1.58	$1.51	87.9%	-1.3%
	Product	12.07	91	Townhouse	2024	1,868	$2,429	$2,214	$1.30	$1.18	95.6%	-4.5%
	Product	12.89	156	Build-for-rent	2023	1,618	$2,621	$2,575	$1.62	$1.74	93.0%	-3.6%
Product comps average	Product	9.64	141	—	—	1,750	$2,574	$2,474	$1.49	$1.47	92.9%	-1.9%
Nearby new-construction (≤5 mi)
	Nearby new	2.04	246	Garden	2023	1,420	—4	$2,328	—	$1.64	94.3%	-3.1%
	Nearby new	3.73	228	Low-rise	2022	1,402	$2,748	$2,758	$1.96	$1.97	92.1%	-0.4%
	Nearby new	3.91	352	Garden	2023	1,435	$2,455	$2,537	$1.71	$1.77	90.9%	-0.5%
	Nearby new	4.99	264	Low-rise	2023	1,368	$2,381	$2,475	$1.74	$1.36	95.8%	-5.4%
Nearby new average	Nearby new	3.67	273	—	—	1,406	$2,5285	$2,524	$1.80	$1.69	93.3%	-2.3%

Main Street Townes at Lilburn – 10 Comps (3BR Asking Rent; green = townhome/BTR, blue = apartment)

How I picked the comps:

Townhome/build-to-rent group (6): 2022–2024 builds with 91–239 units, 4.8–12.9 miles away. There are no comparable townhome communities inside 5 miles, so these come from across Gwinnett and east DeKalb.
Nearby apartment group (4): 2022–2023 garden and low-rise communities within 5 miles that report 3BR rents. They set the price a renter could pay for a smaller new 3BR close by.
Excluded: older garden properties within 3 miles, built 1980s–2000s (more than 15 years apart in vintage, 3BR rents around $1,750–$1,950). I also left out SkyMor Panola, which is mid lease-up at 55% occupancy.

Is the pricing maximizing long-term revenue?

Only partly.

Posted rent has stalled. Yardi market rent was $2,746 in July 2024 and $2,740 in November 2025 . It's up just 2.5% since mid-2024 , even though occupancy hit 100% in August .
New leases have stopped gaining on the leases they replace. New leases came in 7.2% above the prior lease in 2024 and 0.4% below it in 2026 so far (23 leases).
Concessions continue at full occupancy. 9 of 55 new leases since April 2025 included a concession , averaging about $1,550 in Q3 2026 . That brings average new-lease net effective rent down to about $2,671 .
The market backdrop is soft but not weak. Comps' median asking rent is down about 3% over 12 months . Your estimated retention is 74% . You're winning share, so this argues for steady, measured increases rather than a big push.
Expirations are bunched. 45 of the next 68 expirations, or 66%, fall between March and July 2027. June alone has 14, which is 17.5% of the property .

Recommendations

Stop the new-lease concessions and raise posted rent $25–50, starting with the Dupree (C2) plan. C2 lists at $2,901 against $2,761 for the Coleman (C1) plan . Test C2 at the top of that range and C1 at the bottom. At 98.8% occupied there's no vacancy to buy down. Each $25 across the occupied units is worth about $23.7K a year, and $50 is about $47.4K . Stay at or below Generation Avondale East's ~$2,800 3BR rent on a net basis. Above that, renters start comparing you with 1,400 sf apartments.
Use lease-term pricing to spread out the spring/summer expirations. Charge more for terms that end in March–July and offer better rates for terms ending October–February. You're already writing 15–16 month renewals , so make that deliberate. Fewer June move-outs protects occupancy when comp concessions are heaviest.
Set a renewal target rather than holding rents flat. Aim for +2–3% on renewals, below new-lease rent, and hold back on residents with long tenure. I couldn't measure your actual renewal increases (see below), so check your renewal letters against this.

Data gap: I couldn't measure how much you've been raising renewals. In Yardi, the renewal lease records carry the same rent as the original lease in every case (49 of 49 in 2026) , which almost certainly means the field is being copied over rather than updated. Pull renewal-offer data from your revenue management system to confirm recommendation 3 before acting on it.