Douglas County is home to Lawrence, Kansas and the University of Kansas — a classic university town with a rental market shaped by student housing demand, institutional investors, and a mix of local and out-of-state operators. This report maps ownership concentration across 41,584 parcels, identifying 430 clusters with deed-date tenure analysis and out-of-state operator detection.
Important data availability note: The Douglas County public parcel data does not expose assessed or appraised values. This report's concentration analysis is based on ownership patterns, entity density, mailing-address co-location, absentee/out-of-state ownership signals, deed dates (enabling ownership tenure analysis), lot size, property class, and section-township-range notation for rural parcels. Value-weighted analysis is not available for Douglas County. The clustering, entity rosters, and operator mapping are identical in detail to NPA's other county reports.
Douglas County is a university town market where student housing demand creates a distinct ownership concentration pattern — and where deed dates enable tenure analysis unavailable in many other counties.
University town ownership patterns. Lawrence is home to the University of Kansas, and the rental market surrounding campus drives a concentration of multi-entity operators. The 430 clusters identified in Douglas County include operators whose portfolios are concentrated in the student housing corridors near KU — a pattern distinct from the institutional SFR activity visible in metro counties.
Deed dates in YYYYMMDD format: tenure analysis enabled. Douglas County parcel data includes deed dates, which NPA's pipeline converts into ownership tenure calculations. This enables identification of long-held portfolios (operators who acquired properties decades ago) versus recent aggregators (entities that assembled portfolios in the last few years). Tenure patterns often reveal different operator strategies and risk profiles.
Out-of-state operators visible. The Douglas County data reveals ownership concentration clusters linked to mailing addresses outside Kansas. For example, entities with Dallas, Texas mailing addresses appear in the cluster data — confirming that even a mid-sized university town market attracts out-of-state capital. The report identifies every out-of-state cluster with entity counts and parcel counts.
Rural parcels use section-township notation. Douglas County's rural parcels use section-township-range legal descriptions rather than street addresses. NPA's pipeline handles both urban and rural parcel formats, ensuring that rural holdings are included in cluster analysis alongside city parcels.
A complete county intelligence package — PDF report and companion CSV — delivered as instant digital downloads.
Executive dashboard, geographic treemap, scatter analysis, municipality breakdown for Lawrence and surrounding areas, top clusters by two ranking methods, entity rosters for the top clusters, ownership tenure analysis, CSV quick-start guide, full methodology, and legal disclaimer.
Structured data covering every entity in every cluster. Filter by owner state, entity count, parcel count, or deed date for custom tenure analysis. Cross-reference with other Kansas county data.
See which operators control student housing corridors near KU and identify portfolio concentration in Lawrence's rental market.
Use deed-date tenure data to distinguish long-held portfolios from recent acquisitions when evaluating borrower exposure.
Identify when buyer or seller entities trace back to the same ownership clusters controlling multiple Lawrence-area properties.
Identify out-of-state operators who need local management partners for their Douglas County portfolios.
The report is generated from public Douglas County assessor records. Owner names and mailing addresses are normalized, then clustered by mailing address using the same methodology applied to all NPA county reports. Deed dates are parsed from YYYYMMDD format into standardized date fields for tenure analysis. Government-owned properties are excluded from clustering.
For complete methodology details, see the methodology page.
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