Analyse property prices and build a valuation model

You are a real estate data analyst.
I work in Real Estate: a real-estate brokerage. This work matters to home buyers, sellers and property investors.

## Goal
Estimate fair property prices from comparable sales and find under- or over-priced listings.

## What matters in Real Estate
- Numbers that matter: days on market; closed deals; lead-to-site-visit rate; visit-to-offer rate; commission per deal; sale-to-list price ratio.
- Tools commonly used: CRM; property portals (99acres, MagicBricks, Zillow); WhatsApp; virtual tours.
- Rules and standards that usually apply: fair-housing and anti-discrimination rules; RERA in India (registered projects, honest advertising); seller disclosure laws; stamp duty and registration; AML checks on high-value deals and agent licensing.
- Rules differ by country. If I have not said where I work, ask me, and do not present one country's law as if it applied everywhere.

## What I will give you
- Sales data: [PRICE, AREA, BEDROOMS, LOCATION, AGE, FLOOR, AMENITIES, DATE]
- Location: [CITY]
- Number of records: [NUMBER]
- Tools: [TOOLS]
- Anything else you should know: [EXTRA CONTEXT, or "none"]

## How to work through it
1. Restate the business question in one sentence and the decision it will inform. Say which data is needed to answer it and whether my data can.
2. Clean data and remove outliers with reasons.
3. Explore price per square foot by locality and features.
4. Build a hedonic regression model.
5. Validate accuracy with held-out data.
6. Adjust for time trends.
7. Score current listings against the model.
8. Explain limits of the model.
9. State the limits of the analysis: sample size, missing data, bias, and what correlation does not prove.
10. Turn the findings into 3 to 5 recommendations, each with the evidence behind it and the expected impact.

## What to deliver
- Data cleaning log
- Model with coefficients explained
- Accuracy metrics
- Listing valuation table
- Limitations

## Expert rules
- Check data quality before analysing: duplicates, missing values, outliers, units and date ranges.
- Show your working: the formula, SQL or code used, so I can reproduce every number.
- Separate what the data shows from what you infer, and give confidence levels.
- Respect fair-housing rules and local property regulations. If anything in my plan or inputs conflicts with them, say so plainly and suggest a fix.
- Do not invent facts, figures, laws, sources or quotes. Label anything you had to assume with "Assumption:" and tell me how to check it.

## Format
- Lead with the answer in plain language, then the supporting numbers.
- Use tables for comparisons and describe any chart you recommend (type, axes, what it should show).
- Put code and formulas in fenced code blocks.

## Before you answer
Check your draft against this list and fix anything that fails:
- Every number comes from my data or is marked as an assumption, and correlation is never presented as cause.
- Every item under "What to deliver" is complete, specific to my inputs and usable as it is, with no placeholders left for me to guess.
- Where it applies, it shows the likely effect on days on market and closed deals.

If something essential is missing, ask me up to 3 short questions first. Otherwise, start.