Day 12 of 20 · AI for Real Estate

Micro-Level Market Analysis with Real Data

⏱ 7 min 📊 Beginner

This is one of the most powerful lessons in the entire course. On Day 5, you learned how to connect to property data APIs. Today, you'll take that data and turn it into micro-level market analysis that makes sellers say, "No other agent has ever shown me this."

We're talking street-level insights. Not "the national market is doing X" — that's useless to someone selling their home. We're talking "here's what's happening on your street, in your postcode, right now." That's the level of specificity that wins instructions.

What is micro-market analysis?

Most agents talk about the market in broad terms. "The market is strong." "Prices are up 5% nationally." That tells your client nothing useful about their specific situation.

Micro-market analysis zooms in. Way in. It answers questions like:

- What have properties on this specific street sold for in the last 12 months?

- What's the average days on market for this postcode/zip code?

- Are prices trending up or down at the neighbourhood level?

- What's the ratio of sold price to asking price in this micro-area?

- How much inventory is currently available versus 6 months ago?

- What type of buyer is active in this specific area?

When you can answer these questions with real data — not gut feeling — you become the most credible agent in the room. And AI makes the analysis part effortless.

Knowledge Check

Why is micro-market analysis more valuable than national market data?

A

National data is always wrong

B

National data is only useful for economists

C

Sellers care about what's happening on their street and in their postcode, not national averages that may not reflect their local reality

D

Micro-data is easier to find

A national average of "prices up 5%" could mean prices in your client's neighbourhood are up 12% or down 3%. Sellers want to know what's happening within walking distance of their front door. Street-level and postcode-level data gives them the specific context they need to make confident decisions.

Pulling the right data

Building on what you learned on Day 5, here's where to pull micro-market data:

UK agents:

- HM Land Registry Price Paid Data — every recorded sale with price, date, and address

- Rightmove and Zoopla sold prices — searchable by postcode

- Propertydata.co.uk API — average prices, yields, and trends by postcode

US agents:

- Zillow API / Redfin data — sold prices, Zestimates, and market trends

- MLS data — the most accurate and detailed, accessible through your brokerage

- Realtor.com market data — inventory, median prices, days on market by zip code

Australian agents:

- CoreLogic RP Data — the industry standard for Australian property data

- Domain sold prices — searchable by suburb

- SQM Research — vacancy rates, asking prices, and stock on market

All markets: Google Sheets is your friend. Export data, organize it by street or postcode, and you've got a dataset ready to feed into AI.

Feeding data into AI for analysis

Here's where the magic happens. Once you have the raw data, AI turns it into insights.

The micro-market analysis prompt:

"I'm going to paste property sales data for [postcode/zip code/suburb]. Analyse this data and give me: 1) Average sale price and price trend over the last 12 months, 2) Average days on market, 3) Sold price vs asking price ratio, 4) Supply vs demand indicators, 5) The type of buyer most active in this area, 6) A 2-3 sentence summary I can use when talking to a seller. Present this as a professional market snapshot."

Then paste your data. It could be as simple as a table:

|---------|-----------|------|----------------|------|

| 14 Oak Lane | £325,000 | Jan 2026 | 21 | 3-bed semi |

| 22 Oak Lane | £310,000 | Nov 2025 | 35 | 3-bed semi |

| 8 Oak Lane | £340,000 | Sep 2025 | 14 | 3-bed detached |

AI will spot the trends you might miss: prices are rising, the detached sold faster than semis, demand outstrips supply.

🤖

ChatGPT

online

Here's sold data for BS8 (Clifton, Bristol) in the last 6 months. Analyse it and give me a micro-market snapshot I can present to a seller considering listing their 3-bed Victorian terrace. 28 Royal Park - £625,000 - Aug 2025 - 18 days - 3-bed terrace 15 Meridian Place - £580,000 - Sep 2025 - 24 days - 3-bed terrace 42 Belgrave Road - £610,000 - Oct 2025 - 12 days - 3-bed terrace 9 Royal Park - £645,000 - Nov 2025 - 9 days - 3-bed terrace 31 Meridian Place - £598,000 - Dec 2025 - 16 days - 3-bed terrace 22 Belgrave Road - £635,000 - Jan 2026 - 11 days - 3-bed terrace

**Micro-Market Snapshot: BS8 Clifton — 3-Bed Victorian Terraces** **Average Sale Price:** £615,500 **Price Trend:** Upward. Prices have risen from £580-625K in Aug-Sep to £635-645K in Nov-Jan — approximately 5-7% growth over 6 months. **Average Days on Market:** 15 days **Trend:** Accelerating. Properties are selling faster month-on-month — from 18-24 days in summer to 9-11 days in recent months. This indicates increasing buyer demand. **Sold Price vs Asking Price:** Based on these figures, properties in BS8 are consistently achieving at or above asking price. The rapid reduction in days on market suggests competitive bidding. **Supply vs Demand:** Demand is clearly outpacing supply. The declining days-on-market metric combined with rising prices is a classic seller's market signal. **Active Buyer Profile:** Professional couples and families with budgets of £580-650K. Likely local upgraders or professionals relocating to Bristol. --- **Your Seller Talking Points:** "The Clifton market for 3-bed terraces is the strongest it's been in months. Properties like yours are selling in under two weeks, prices have climbed 5-7% since the summer, and demand is outstripping supply. If you're considering selling, the timing couldn't be better — and based on recent comparables, I'd be looking at a guide price of £630-650K for your property."

↻ Replay conversation

Knowledge Check

What's the most powerful way to use micro-market data in a seller valuation meeting?

A

Print out national market statistics

B

Show them data from a completely different area

C

Show them street-level sold data with trends, days on market, and sold-to-asking ratios specific to their neighbourhood

D

Tell them what you think based on experience

Nothing builds credibility like showing a seller exactly what properties on their street and in their postcode sold for, how quickly they sold, and what the trend direction is. This is specific, data-driven advice that no amount of general market chat can match. It's the difference between "the market is good" and "your neighbour sold for £635K in 11 days last month."

The one-page market snapshot

Your goal is to create a single, clean document you can hand to a seller during a valuation. Here's the structure:

Page title: Market Snapshot — [Postcode/Suburb/Neighbourhood]

Section 1: Recent Sales — A table of the 5-8 most relevant comparable sales with address, price, date, and days on market.

Section 2: Key Metrics — Average price, price trend (with % change), average days on market, and sold-to-asking ratio.

Section 3: Market Commentary — 3-4 sentences of AI-generated analysis explaining what the data means for this seller.

Section 4: Recommended Price Range — Your suggested guide price based on the data, with justification.

Prompt to generate the commentary:

"Based on this data [paste data], write a 4-sentence market commentary for a seller in [area]. Tone: confident, data-driven, and reassuring. End with a sentence about recommended pricing."

Print it. Put it in a branded folder. Hand it to the seller. Watch their face. No other agent showed up with this level of preparation.

One-Page Market Snapshot — Recent Sales, Key Metrics, Market Commentary, Recommended Price Range

A one-page micro-market snapshot turns raw data into a powerful valuation tool.

Knowledge Check

What should a one-page market snapshot include for a seller valuation?

A

A list of all your recent sales

B

Recent comparable sales, key metrics with trends, AI-generated market commentary, and a recommended price range with justification

C

Just the recommended price

D

National house price index data

The one-page snapshot combines hard data (recent sales, metrics) with intelligent analysis (AI commentary, pricing recommendation). It's specific to their neighbourhood, backed by real transactions, and positions you as a data-driven professional. This single document can be the difference between winning and losing an instruction.

Making this a repeatable system

The beauty of this approach is that once you've built the workflow, you can create a micro-market snapshot for any address in minutes:

1. Pull recent sales data for the postcode or street (5 minutes using the sources you set up on Day 5)

2. Paste into AI with the analysis prompt (30 seconds)

3. Drop into a template — a simple Word doc or Google Doc with your branding (2 minutes)

4. Print or send as PDF before the valuation appointment

Total time: under 10 minutes. Total impact: massive.

Do this for every valuation appointment. Do it for every buyer consultation. Do it when a past client asks "what's my home worth now?" The data is free. The AI analysis is instant. The competitive advantage is enormous.

🏠

Day 12 Complete

"Micro-market analysis is your secret weapon for winning instructions. Pull real data, let AI spot the trends, and show up to every valuation with street-level insights that no other agent can match."

Tomorrow — Day 13

Follow-Up Messages That Close

Tomorrow you'll master the art of follow-up messages that keep you top of mind and close more deals.

Continue to Day 13 →