How we measure property managers.

Every operator in our coverage markets is evaluated against three eligibility tests before a scorecard is produced. The tests are designed to filter single-rental owners and one-off listings while admitting operators with meaningful market presence.

A property manager qualifies if all of the following are true:

1. At least 30 listings observed in the trailing 12 months (anchored to our data refresh date for the market).
2. At least three distinct addresses or at least one community with thirty or more listings.
3. At least one currently-active listing (still active or deactivated within the last 90 days).

The two-pronged second test admits both scattered-site operators (who hit the diversity threshold through breadth) and single-asset multifamily operators (who hit it through depth at a single community).

Earlier methodology versions labeled this window “T6M” on the market headline tile, which was a labeling drift; the actual eligibility filter has always been T12 in production. v0.6.3 (Patch 2) corrects the surfaced label so it matches the underlying computation. No operator gains or loses eligibility from the relabel.

We do not maintain category exclusion lists. The thresholds above filter out non-operator listings naturally. If future markets surface edge cases that slip through, we document the exclusion rule then and version the methodology accordingly.

The URU is the four-level hierarchy Dwellsy assigns: building or community → address → unit → room/bed. All metrics in this scorecard operate on the unit level (the third tier), aggregated up to the operator's full portfolio.

URU resolution happens upstream of this scorecard in Dwellsy's core data infrastructure. The scorecard consumes resolved URUs as inputs — it does not derive them.

Operator typemeasures how the operator's portfolio is organized — whether their units are concentrated in multi-unit communities (multifamily buildings, build-to-rent communities, condo developments) or distributed across individually-managed single-family rentals. v0.6.2 refines the v0.6.1 three-way split into a four-way axis by splitting MF/BTR by median community size.

We group the operator's portfolio by community and count distinct units the operator manages at each community. A community is concentrated if the operator manages 10 or more units there. From that:

• SFR (Scattered) — concentrated share < 30%.
• Small MF/BTR — concentrated share ≥ 70% AND median concentrated community size 10–49 units.
• Large MF/BTR — concentrated share ≥ 70% AND median concentrated community size ≥ 50 units.
• Hybrid — concentrated share between 30% and 70% (no scale split).

Why the Small vs Large MF/BTR split. Lender and acquirer prospects care about MF/BTR community size as a structural distinction. A 200-unit Class A operator has a different risk profile and different acquisition profile than an operator running 8-unit walk-up small MF. The v0.6.1 five-cell taxonomy collapsed these into one MF/BTR bucket; v0.6.2 makes the distinction visible.

Scale(Institutional vs Independent) measures the operator's footprint. An operator is Institutional if they manage 500 or more distinct units across all Dwellsy IQ coverage markets in the trailing 12 months, Independent otherwise. The 500-unit threshold is a judgment call; in practice it cleanly separates names that operate at scale requiring institutional capital structures from established local and regional operators.

Scale classification considers an operator's observed presence across all Dwellsy IQ coverage markets, not just the market in which a given scorecard is published. Operators are Institutional if their combined trailing-12-month observed units across all our covered markets meet or exceed 500. This rule lets us recognize national operators whose footprint in any single market falls below the threshold but whose cross-market scale is substantial. The Hybrid bucket does not carry a scale split — a Hybrid operator is simply Hybrid regardless of cross-market urus.

Figure 1. The v0.6.2 seven-cell taxonomy. The type axis (rows) splits operators into SFR, Small MF/BTR, and Large MF/BTR by concentrated share and median community size. The scale axis (columns) splits each type into Independent and Institutional by cross-market urus. Hybrid carries no scale split — it is its own classification. Cell colors match the quadrant badges used elsewhere on the scorecard.

The taxonomy is structural, not evaluative. Each cell contains operators of varying quality. The classification answers “what kind of operator is this?” — the rest of the scorecard answers “how well do they operate?”

Why this measure is structural to operator type. Single-family operators cannot meaningfully cherry-pick which inventory they show on Dwellsy. Every property is unique. A renter searching for a three-bedroom house in a specific neighborhood is looking for that specific home with its specific layout, yard, and location — the operator cannot substitute Property B for Property A. To capture any rental, the SFR operator must list it. The cherry-picking risk is structurally low.

Multifamily and BTR operators sit on undifferentiated inventory in a leasing office. A community with 20 vacant two-bedroom units can list five and route walk-in prospects to the rest. The cherry-picking option is structurally available, and some operators use it — historically as a strategy to control which units appear in third-party search results.

Community Visibility measures whether this is happening.

Scope. We compute Community Visibility for operators who meet three conditions: at least one community where they manage 30 or more units, at least 50% of their inventory in concentrated communities, and at least 12 monthsof listing history at those communities. Operators who don't meet all three conditions don't have this section on their scorecard — for them, the question is either unanswerable (Scattered operators, where there's no honest denominator) or not yet measurable (operators below the tenure gate).

Formula.For each qualifying community, we compute the operator's expected listing volume in the trailing 12 months based on the community's true unit count (the structural community-size field from Dwellsy's core data, present in every listing row) and a default annual turnover rate of 20%. We compare that expectation to the operator's actual listing count.

The 20% turnover assumption matches the empirical cross-market norm across Chattanooga, Jacksonville, and Nashville under v0.6.1 — and aligns with the U.S. national rental-household mobility rate.

The ratio answers:

“Of the units that should have plausibly turned over and been listable in T12, how many did this operator actually list?”

Three-state taxonomy (v0.6.1, unchanged in v0.6.2). v0.6 simplified the Community Visibility output to three states. The legacy fourth state (“above expected — comprehensive coverage”) was retired because it implied a comparative judgment the data couldn't support; a visibility ratio above 1.0× simply means the operator is listing comprehensively at higher-than-default turnover, which is a positive signal but doesn't warrant a separate color-coded tier.

Reported states:

• Partial visibility (ratio <0.5×) — observed listings represent less than half of expected. Notably reduced visibility relative to community structure.
• Likely partial visibility (0.5–0.8×) — most but not all expected listings present. Possible normal turnover variation, possible selective listing.
• Comprehensive visibility (≥0.8×) — listings cover the substantial majority of expected turnover. Within expected range for a fully-transparent operator.

Ratios materially above 1.0× are meaningful signal — they identify operators visibly more transparent than the cohort norm, which is a credibility-positive signal. Institutional Class A MF communities typically turn over faster than the cohort average; a 2.1× visibility ratio for an operator like UDR reads as the operator genuinely listing comprehensively.

Longer tenancy reflects multiple compounding operator behaviors — tenant screening, property condition, responsiveness, fair renewal pricing — and is one of the cleanest behavioral signals in the scorecard.

We measure tenancy at the unit level using episode clustering. For each unit, we sort all listings by creation date and group consecutive listings into episodes — sequences where the next listing's creation falls within 180 daysof the prior listing's deactivation. The gap between consecutive episodes on the same unit approximates the tenant's stay.

Per-operator tenancy is the unit-weighted medianof all observed tenancy gaps across the operator's portfolio. We use median rather than mean because lease-length distributions are right-skewed (a small number of very-long stays would otherwise inflate averages). Units with only a single observed episode don't contribute to the calculation — we don't infer tenancy without a measurable gap.

Reported in months, rounded to one decimal.

We bucket trailing-six-quarters listings by quarter, compute median rent within each bedroom bucket (1-bedroom, 2-bedroom, 3-bedroom-and-up), and average across buckets weighted by the operator's overall bedroom mix. The result is the mix-adjusted median rent per quarter.

The trajectory chart shows the last six quarters. The headline YoY change is the percentage difference between the most recent quarter and the same quarter one year prior.

Formula.We compute the operator's mix-adjusted YoY rent change (from §06) and subtract the MSA cohort median YoY change over the same period. Operators whose rents grew faster than the cohort median are positive on Rent Performance. Operators who lagged the cohort are negative.

This isolates operator pricing capability from inherited portfolio quality. Every operator in the cohort is compared to the same peer-group baseline during the same period. Class A operators are not rewarded for managing high-rent inventory; they are rewarded only when they push rents faster than other Class A operators (who would be reflected in the cohort median). Similarly, Class C operators aren't penalized for low rent levels — only for failing to push rents at peer rates.

Confounders we disclose.The metric is meaningful but noisier than DOM or Tenancy. We control for bedroom mix but not for square footage, neighborhood, building age, or amenity differences within an operator's portfolio. Three real noise sources:

• Submarket exposure (operators concentrated in gentrifying neighborhoods see faster growth regardless of skill).
• Mix shift within the trailing window (an operator adding higher-rent properties mid-window shows artificial growth).
• Capital events (operators who renovated mid-window push rents through investment, not pure leasing skill).

We weight Rent Performance at 10% of the composite ranking to reflect these confounders. A future version (v0.7) will refine the metric to compare only units that appear in both periods — eliminating the mix-shift confound and likely justifying a heavier weight at that point.

Market rent growth aggregate.

Each market displays a median year-over-year rent growth figure computed across the ranked operators in that market. The figure represents the typical ranked operator’s portfolio rent trajectory over the trailing 12 months. The benchmark line (“vs national”) compares the market value against the median across all ranked operators across all coverage markets — a single national number that does not vary by market.

Computation.Per ranked operator, the YoY rent change is computed as the median listing-rent change year-over-year across that operator’s portfolio. The market aggregate is the median of those per-operator values, equal-weighted. Operator-equal weighting was chosen over unit-weighting for v0.6.3 to keep the metric interpretable — “the typical ranked operator in this market.” Unit-weighted alternatives are under consideration for v0.7.

The market-vs-national delta is pre-computed at seed time in percentage points and surfaced as a tile benchmark line (green if >+0.2 pp, orange if <−0.2 pp, neutral within the band). Submarket-level rent growth is not computed in v0.6.3 — listing-level geographic aggregation with minimum-N controls is a v0.7 candidate. Under a submarket filter the headline tile retains the MSA-wide value with an explicit scope annotation.

State-level aggregates.

State-level aggregates pool ranked operators across all MSAs in a state and compute medians across the pooled set. Each ranked operator contributes one value to the state median, regardless of which MSA they appear in. Operators who appear in multiple MSAs (e.g., institutional operators with footprint across several markets) are currently counted once per MSA they appear in. Cross-market operator-identity dedup is on the v0.7 roadmap. State-level active operator counts are similarly a raw sum across MSAs and may double-count multi-market operators.

The state landing pages at /property-managers/[state] surface four operator-weighted tiles — active operators (sum across MSAs), eligible for ranking (sum across MSAs), median DOM T12 (operator-weighted median across the pooled in-state ranked operators), and rent growth T12 (operator-weighted median of pmYoyChangeacross the same pool). Median DOM and rent growth carry a “vs national” benchmark line where the national reference is the operator-weighted median across every ranked operator in every covered MSA — the same single national number that Patch 3 already computes for market-level rent growth, and its DOM analogue computed at runtime.

State pages are an additive feature on top of v0.6.3 base — no methodology version bump. Single-MSA states (Florida, Arizona in the v0.6.3 footprint) get the same UX as multi-MSA states; the state page renders one MSA card. As coverage expands and adds new MSAs in already-covered states, the state page auto-updates without any data-layer changes: state membership is derived from each market’s state field.

Share trajectory.

Share trajectoryshows how this operator’s share of ranked-cohort listing activity has changed year-over-year. The metric is computed across continuing operators with substantial presence in both periods (at least 30 listings in each), normalized to share of the cohort’s total listings so that proportional pipeline expansion across all operators produces a 0% trajectory. Real movement indicates relative gain or loss of market position.

Continuing cohort. Operators with at least 30 listings in BOTH the trailing 12 months (T12) AND the prior 12-month window (T24-T12). Operators outside the cohort fall into one of two display categories: Newly tracked(≥30 listings T12 but <30 in the prior window) or New operator (zero prior listings). These operators see a context pill in place of a comparison number; their data is excluded from the cohort median.

Why share rather than absolute? An earlier version of the metric computed absolute year-over-year listing-count change. A pressure test surfaced three biases that made the absolute version unusable: pipeline-coverage expansion (every operator appeared to grow even if they did nothing), thin-baseline noise (operators with 1 listing in the prior period produced absurd growth percentages), and survivor bias (operators that shrank to zero between periods were systematically excluded from the median). The share-based reframe addresses the first two biases directly and partially addresses the third. The pressure-test results, post-revision, show plausible directional signal consistent with known market dynamics — Phoenix at +10.07% (established operators consolidating), Memphis at −9.89% (SFR aggregators entering aggressively), Clarksville at −15.81% (heaviest fragmentation in the v0.6.3 footprint).

Why no star treatment? Share trajectory is a contextmetric, not a performance one. A higher share isn’t reliably better: longer tenancies → fewer relistings → lower share (good operationally, lower share); improving operationally drops the share via the same mechanism. M&A activity, portfolio composition shifts, and new entrants all move share without reflecting operator health. Star treatment requires a metric where “higher = better” is reliably true; share trajectory fails that test. The scorecard shows the metric with cohort + national context and methodology disclosure so readers can form their own judgment.

Residual caveats.Coverage bias is only neutralized if pipeline improvements affected all continuing operators uniformly — non-uniform improvement (e.g., a new ingestion source biased toward aggregators) would still distort. Survivor bias persists for operators that shrank to zero between periods; v0.7 backlog includes a “departed” classification to surface them. Listing-level re-listing methodology affects numerator and denominator alike but is a counting artifact worth acknowledging. The metric is shown for context and is not used in ranking or composite scoring.

Canonical operator identity.

Operators that appear in multiple markets in our coverage are identified via name normalization — stripping common business entity suffixes (LLC, Inc., Ltd., Co., Corp.) and trailing punctuation, lowercasing, and collapsing whitespace. When two or more PM records match the same normalized name across distinct markets, they’re grouped into a single canonical operator entity. This enables cross-market operator profiles at /operators/[canonical-slug] and dedup’d state-level operator counts.

Conservative normalization.The normalization is intentionally conservative — operating- brand modifiers like “Property Management” or “Realty” are not stripped, so distinct operating brands under a common parent (e.g. “JWB Rental Homes” vs “JWB Real Estate Capital”) remain separate canonical entities. Manual parent-subsidiary mapping for true roll-up acquirers is a v0.7 candidate.

Manual review of false positives.Before the auto-detected matches are baked into the seed, the canonical mapping is reviewed for false positives — generic names that happen to match across markets but represent different operators (e.g. “Trinity Management Company” in two markets refers to two unrelated companies, manually excluded from canonical mapping). Excluded operators stay as separate per-market entries.

State-level count dedup. v0.6.3 Patch 5 state-level operator counts summed per-MSA values, which double-counted multi-market operators. v0.6.4 dedups bycanonicalOperatorId— a multi-market institutional operator that appeared in Nashville + Memphis + Clarksville counts once in Tennessee’s state-level total. The dedup applies to the ranked cohort we carry full data for; unranked operators in the broader ≥3-T12 universe don’t receive canonical mapping in v0.6.4.

Concession activity.

For every PM in coverage, the v0.6.4 Patch 2 classifier scans the T12 listing descriptions for stereotyped concession language — “one month free”, “move-in special”, “no deposit”, percent-off promotions, and similar patterns — and computes the share of T12 listings that mention at least one. The result is surfaced on scorecard Layer 5 as operator concession rate, with the market median across ranked operators as cohort context.

Regex-based, v1 catches stereotyped language. Detection is pattern-matching, not semantic. The v1 dictionary covers about a dozen pattern families (free month, percent off, dollar off, no/reduced deposit, move-in special, explicit concession, rent reduction, lease special, limited- time offer, waived fee, free rent). Indirect or paraphrased language (“ask us about specials” without naming the special) will be missed. A v2 LLM-grader pass is on the v0.7 backlog.

Context, not ranked.Concession activity does not feed the composite ranking and does not award stars. It’s presented as a present-tense signal of demand or supply stress at the operator level — high participation can mean any of: aggressive lease-up, soft submarket, large institutional discounting program. Read alongside DOM, rent growth, and share trajectory rather than in isolation.

Cohort comparison.The market median is computed across ranked operators with a non-null concession rate (operators absent from the classifier input — typically no T12 description data — are excluded from the median so they don’t pull it toward zero). Operators more than 20 percentage points above the median receive an orange accent on Layer 5 (signaling elevated concession activity vs the cohort); operators more than 20pp below get a green accent (low concession activity).

Three subscores, each on a 0–100 scale, are computed from trailing-12-month listings:

• Completeness — percentage of listings with non-null values for rent, bedrooms, bathrooms, square footage, description, amenities, and at least one photo. Each missing field deducts proportionally.
• Amenities — median count of amenity entries per listing, cap-normalized (20 amenities = 100).
• Description Length — median description character count, cap-normalized (500 characters = 100).

The reported Marketing Quality score is the average of the three subscores. Operators with consistently well-prepared listings score in the 80s and 90s. Operators with sparse data, missing photos, or threadbare descriptions score lower.

Weights for operators with Community Visibility computed:

Weights for operators without Community Visibility (the section is suppressed for Scattered and Hybrid operators below the visibility gate, and for MF/BTR operators under the 12-month tenure threshold):

The 15% normally allocated to Community Visibility redistributes proportionally to the other four components. Both schemes sum to 100%, so composite scores remain comparable across the full cohort.

Marketing Discipline (15%) and Community Visibility (15%) are secondary but meaningful signals. Marketing Discipline reflects listing-side rigor; Community Visibility reflects transparency. Both are real quality differentiators, both are harder to game than they look, and both deserve weight without dominating.

Rent Performance (10%) is included as a pricing-skill signal but weighted lower than the cleaner metrics due to its documented confounders.

What we do not weight. Rent level. Portfolio quality. National scale beyond the MSA. These are descriptive characteristics, not performance signals. We surface them as context but do not let them drive operator rank.

Star system (v0.6.2).

v0.6.2 replaces percentile-rank tier labels (top decile, lagging quartile) with a binary star system. Per-metric stars surface across the v1.0 design — Layer 1 cohort qualifier, Layer 2 headline tiles, Layer 3 card headers, Layer 4 signal subcards.

• 🌟 Gold star — top quartile of the applicable cohort (≥75th percentile).
• ⭐ Silver star — second quartile (50th–75th percentile) — above-median position within cohort.
• No star— below the 50th percentile. The cohort qualifier still renders (“Present in cohort”) but no star icon. This reinforces operator dignity — top performers earn stars; others simply have no star.

Cohort hierarchy. Star assignment requires choosing which cohort to compare against. v0.6.2 pre-computes three percentile ranks per metric per PM and selects the applicable cohort via a fallback waterfall:

1. Primary cohort — same MSA + same 7-cell quadrant. Used if N ≥ 10.
2. Fallback cohort — same MSA + same operator type (SFR / MF/BTR / Hybrid), any scale. Used if primary N < 10 and fallback N ≥ 10.
3. MSA cohort — all eligible operators in the same MSA. Used as the final fallback.

The cohort label displayed in the scorecard (e.g., “Gold star · Chattanooga SFR Independent cohort”) reflects whichever level was actually selected.

Lending Signals.

Five auxiliary signals surface alongside the composite in Layer 4 of the v1.0 design. They’re underwriting-relevant synthesis metrics designed for a 30-second scan by lender/acquisition teams; they don’t feed the composite ranking.

• Vacancy Signal — fraction of the average leasing cycle spent vacant, computed from DOM and tenancy: (DOM_days/30) / (Tenancy_months + DOM_days/30) × 100. Lower = more favorable. Star uses cohort percentile.
• Rent Stability — standard deviation of trailing-12-quarter YoY rent change in percentage points. Lower volatility = more consistent rent posture. Requires 12 quarters of mix-adjusted data; suppressed for operators with shorter history (display: “Insufficient observation history to compute”). Star inverted (lower volatility = top quartile).
• Operator Stability — composite surfacing yearsVisible (length of observation in Dwellsy IQ data) and market count (cross-market footprint). Persistent eligibility per window is a v0.7 component.
• Geographic Concentration — top-3 city share of observed urus, with cohort median for context. Linear position indicator — no star, descriptive only. Concentration is neither inherently favorable nor unfavorable.
• Pricing Tier— operator's latest mix-adjusted median rent positioned within the MSA rent distribution. Premium (≥75th percentile) / Mid-market (25–75th) / Value (<25th). Positional label, not evaluative.

Rent Stability and Geographic Concentration are pre-computed at seed time (v0.6.2 Patches 4 + 7). The other three are derived at render time from existing seeded fields.

Things we measure cleanly. Lease-up speed, tenant retention, listing data completeness, multifamily/BTR transparency, basic portfolio classification.

Things we measure with caveats.Rent Performance carries known confounders (submarket exposure, mix shift, capital events). Composite rankings for operators with very thin data may favor small-sample outliers — we flag these in the rationale text but don't yet mathematically discount them.

Things we don't yet measure.

• Operator transparency for Scattered (SFR) operators. The cherry-picking question is unanswerable for SFR operators in the listings data alone — there is no external denominator we can construct. SFR Credibility is deferred to v1.x, pending claim-flow portfolio attestation.
• National scale beyond our covered markets. Operators with substantial portfolios in markets we don't yet cover may classify as Independent under our methodology even when they operate at institutional scale nationally. Resolution path: expanded market coverage and operator portfolio attestation via the claim flow.
• Granular unit quality (square footage, amenities, year built, condition) beyond bedroom count.
• Submarket exposure within an MSA.

Things this scorecard cannot tell you. Whether the operator will renew their lease with you. Whether a specific unit is well-maintained. Whether the operator is currently for sale or in a transition. Whether market-level conditions are favorable.

Observation precision (v0.6.2).

Every figure on a scorecard is qualified as observed, not total portfolio. The seed surfaces three distinguishable unit-count fields per PM so templates can phrase precisely:

• urusT12 — distinct units observed listing in the trailing 12 months. The smallest, most-conservative number.
• observedCommunities — count of concentrated communities where we observe the operator listing.
• observedCommunityTotalUnits— sum of the top-down PM-managed unit counts across those observed communities. A proxy for portfolio scale at those locations — not a claim about the operator's full portfolio.

Templates phrase explicitly: “managing 8 observed large multifamily communities in the Nashville MSA — communities totaling approximately 2,400 units, with 1,069 distinct units observed listing in trailing 12 months.” We never claim “manages 1,069 units” or “operates 2,400 units” — both would imply we know the operator's full portfolio.

Operator-dignity language gate.

Every auto-generated string — executive summaries, distinguishing characteristics, map narratives — passes through a dignity-language validator at seed time. Forbidden tokens include weak, poor, strong, excellent, underperforming, manages X, operates X. Acceptable replacements use quartile language and observation qualifiers: “Gold star · Lease-up Performance, top quartile in cohort” rather than “Strong leasing performance.” “5 communities observed in our coverage” rather than “Their portfolio of 5 communities.” The system measures; it does not editorialize.

Deferred to v0.7+.

The following improvements are tracked for future releases:

• Kaplan-Meier-style tenancy right-censoring correction — replaces the v0.6.2 short-history caveat with a mathematical adjustment.
• Rent Stability data pipeline — compute volatility from raw listings over 12 quarters rather than from the 6-quarter pre-computed trajectory.
• Same-unit-controlled Rent Performance — eliminates the mix-shift confound; likely justifies a heavier composite weight.
• Minimum-N confidence multiplier on composite — mathematically discounts thin-data operators (currently surfaced via rationale text only).
• SFR Credibility instrument — currently a placeholder; unblocks when claim-flow portfolio attestation provides external scope data.
• Submarket-aware peer cohorts — geographic compatibility threshold activates when major-metro markets with submarket data are added.
• Cross-market national institutional classification — when 8-10 markets are covered, the multi-market aggregation gets accurate enough for national operators with thin per-MSA presence.
• Operator-identity reconciliation — replaces the v0.6.2 name-equality cross-market join with a proper identity table.
• Persistent eligibility per window — the third component of Operator Stability not yet seeded.
• Bedroom-mix portfolio composition and BR-bucketed pricing data — needed for Layer 5D composition and Layer 5F pricing per-bucket.
• Operator dispute / appeal process — once operators see scorecards publicly, the dispute process needs definition and execution.

Material changes — new metrics, re-weightings, threshold shifts — bump the version. Cosmetic changes do not. Prior versions remain accessible, and every scorecard carries the version it was computed under so historical scorecards can be interpreted in their original frame.

Recent versions:

Data is refreshed monthly. The current snapshot reflects listing activity through Jun 29, 2026.