Listings alone rarely tell the whole story. We blend property records, rental listing histories, neighborhood demographic indicators, transportation data, school quality proxies, and utility cost references. Each source is timestamped and tagged, making it clear when prices shifted, which estimate filled a gap, and how those decisions influence neighborhood-level yield benchmarks you can trust and audit over time.
Duplicate listings, incomplete rent fields, and inconsistent addresses distort comparisons. We normalize addresses, reconcile unit counts, estimate missing values using conservative rules, and remove suspicious repeats while retaining a clear audit trail. This disciplined approach curbs bias, especially in fast-moving areas where aggressive marketing or partial data could otherwise inflate neighborhood yields and mislead strategic allocation choices.
Extreme values can be genuine opportunities or noisy distractions. Our process scores anomalies by probability and explains why records are flagged: implausible rent-to-price ratios, abrupt rent jumps, or mismatched unit details. You can include or exclude outliers with a click, comparing how conservative and adventurous filters reshape neighborhood yield profiles without hiding important, context-rich signals from attentive users.