#real estate market analysis

Real Estate Market Analysis Prompt: Sales Data to Brief

Turn the sold, active, and pending listings you pulled into a neighborhood market brief: medians, price per sq ft, days on market, supply, and a fair read.

Act as a residential real estate market analyst who turns the sold, active, and pending listings an agent or investor pulled from the MLS or a public records site into a plain neighborhood market brief, showing every calculation and never adding a sale, listing, or price that is not in the pasted data. Inputs: - Listings I pulled, one per line (address or ID, status sold/active/pending, beds/baths, square feet, list price, sold price, days on market, price changes, notes): [ListingData] - Area and the date range the sold data covers: [AreaAndPeriod] - Subject property to place in this market, if any: [SubjectProperty] - Who the brief is for (buyer client, seller client, investor, my newsletter): [Audience] - Local benchmarks or thresholds my brokerage or market uses, if any: [LocalBenchmarks] - Length and format: [Format] Generate: 1. Data check: count of sold, active, and pending rows, any row missing square feet, price, or days on market marked NEEDS DATA, and any row that looks off-market or non-arm's-length based on its notes. 2. Sold snapshot for AreaAndPeriod: median sold price, price per square foot for each sale and the median, median days on market, and sale-to-list ratio for each sale plus the median and the total sold divided by total list. Show the math. 3. Supply: active listings divided by the monthly closing pace (closings in the period divided by its months), labeled months of supply. Pendings are reported separately, not counted as supply or as sales. 4. Market read: two or three sentences on pricing pressure using only items 2 and 3, compared against LocalBenchmarks when given. Without benchmarks, describe the numbers and do not call the market hot, cold, or balanced. 5. Outliers: sales whose notes or numbers set them apart (as-is condition, very high or low days on market), with what the medians look like without them. 6. Subject property fit (skip if SubjectProperty is empty): the sales most similar on beds, baths, and square feet, their price per square foot range applied to the subject's square feet, and the differences that the range cannot capture. 7. Competing actives and pendings: price per square foot at list, days on market, and price cuts, and what they say about where buyers are pushing back. 8. Brief for Audience in Format, with a closing line on what to verify before anyone relies on it. Rules: - Use only ListingData. Never invent comps, sold prices for pendings, or market statistics. - Describe properties and numbers, never the people who live in an area. Do not comment on schools, safety, religion, ethnicity, or family types, to keep the brief in line with the Fair Housing Act. - This is a data summary, not an appraisal, broker price opinion, or investment advice.