Housing market research

Compare defined markets over comparable periods. Keep market aggregates separate from individual property valuations and personalized financing decisions.

Setup

Set CRAWLORA_API_KEY to your key from crawlora.net. Run the bundled scripts/crawlora.sh from this skill directory or by absolute path. It sends x-api-key to https://api.crawlora.net/api/v1; keep the key in the environment. Read reference/endpoints.md for dataset discovery/history and optional live Redfin context.

Build a comparable market panel

1. Define geography level, candidate places, property type, period, and decision. Dataset region_type accepts national, metro, county, city, or zip. Use /datasets/housing-markets/facets and /search under the same prefix to resolve exact regions and table_id; names and ZIP codes are not table IDs.
2. Start with latest=true, a single region level, and a single property type. Latest is per series, so compare returned period_begin AND period_end. Select a common period if series differ. A monthly observation can summarize a longer rolling window; do not call every row one month's transactions.
3. Retrieve /datasets/housing-markets/items/{region_type}/{table_id} with history=true and the same property_type for the stored chronological series. Keep gaps, window lengths, and revisions visible. Use matched periods for YoY comparisons; overlapping windows are not independent observations.
4. Compare median sale/list prices, inventory, homes sold, and median days on market where populated. MoM/YoY fields are fractions: 0.05 means +5%. Preserve nulls; a thin market or unmatched metric is not a measured zero.
5. Treat affordability as a modeled comparison. Retain income_vintage, income_geo_id, and mortgage_rate_pct. Check the income geography actually matches the intended comparison; unavailable or broader-area income cannot establish precise neighborhood affordability. The dataset's salary estimate assumes 20% down, a 30-year fixed mortgage, 1.5% annual taxes/insurance, and a 28% payment-to-income ratio. Disclose these assumptions and the income lag.
6. If requested, supplement with /redfin/search?location=... or live regional trends. These use numeric region_type and region_id values resolved by the live search response; dataset string types/table IDs are not a drop-in substitute. Live listings and historical market aggregates have different populations and dates; do not blend them into a single unlabeled statistic. Live trends can be display strings such as $1.2M or +5%; they are not the dataset's numeric dollars and fractional change fields.

scripts/crawlora.sh /datasets/housing-markets/search \
  region_type=city state_code=TX latest=true \
  'property_type=Single Family Residential' page_size=5
# Resolve table_id before requesting history for the same region/property type.

Deliverable and interpretation

Return a comparison table and, when helpful, time-series charts with region, property type, full measurement window, units, sample/activity counts, source, collection time, and missing-data notes. Explain tradeoffs against the user's criteria. Link the Redfin source and methodology alongside record provenance.

• Do not average ZIP/city medians to obtain a metro median, sum overlapping geographies, or sum overlapping rolling-window sales counts.
• A change in median price can reflect the mix of homes sold. It is not the appreciation rate of a fixed property. Low transaction counts weaken comparisons.
• Median-income affordability is not an individual's mortgage qualification or monthly budget. Keep custom scenarios and assumptions separate from returned data.
• Bound dataset pagination to page_size<=100 and 10,000 results. A 404 item can mean an absent region/period/property-type combination. Back off on 429, retry transient 5xx once, stop on 401/403, and check application code.