Best AI for Real Estate Wholesalers: Underwrite a Deal Before You Call

A wholesaler can use AI to organise comps, set out the arithmetic for a maximum allowable offer and flag gaps in the repair scope, so a deal is screened in about fifteen minutes before the first call. It cannot verify sales, see the property or know local rules. ARV, repair costs and licensing must still be checked by hand.

The right mental model is a fast junior analyst who is good at tidying data and sums, and bad at knowing whether the data is true. You do the verification and the judgement; the assistant does the formatting, the arithmetic and the "what did I forget" prompts. Everything here assumes a US deal, and state rules on wholesaling, licensing and outreach differ, so verify those locally before you market anything.

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The screening sequence in six steps

1. Collect the facts you actually have. Address, beds, baths, square footage, year built, condition notes from the seller, asking price, and any photos or listing text.
2. Pull comps from a source you trust. Records, listing data or an agent's list. AI does not find comps for you.
3. Have AI tabulate and adjust the comps. Same format, price per square foot, adjustments shown as lines you can challenge.
4. Estimate repairs in categories. From the seller's notes and photos, with a range and a contingency.
5. Run the offer arithmetic. ARV, repairs, your fee, the end buyer's margin.
6. List what you do not know. Title issues, access, occupancy, liens and anything the seller left out.

Steps 3 to 6 are where an assistant saves time. Step 2 and the final list are where you earn the fee.

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Worked example: a three-bedroom with a 70% sanity check

Illustrative. A seller offers a 1,300 sq ft three-bedroom, built 1974, with a dated kitchen, an older roof and a tenant moving out. The asking price is $190,000. The wholesaler pulls five sold comps from the last six months and pastes them in.

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Below are five sold comparables for a subject property. Subject: 3 bed, 1.5 bath, 1,300 sq ft, built 1974, renovated condition is the target. Build a table with sale price, sale date, sq ft, price per sq ft, beds/baths, condition (as I state it), and distance. Then show the average and median price per sq ft, and a range for ARV using the subject's 1,300 sq ft. Show the arithmetic. Do not add any comps. Flag any comp that is an outlier and say why. List the three adjustments you would want to make and what information you need for each.

COMPS:
1) $262,000, sold 2 months ago, 1,250 sq ft, 3/2, renovated, 0.3 mi
2) $248,000, 3 months ago, 1,340 sq ft, 3/1.5, renovated, 0.5 mi
3) $271,000, 1 month ago, 1,400 sq ft, 3/2, renovated, 0.4 mi
4) $205,000, 5 months ago, 1,300 sq ft, 3/1.5, dated condition, 0.2 mi
5) $289,000, 6 months ago, 1,650 sq ft, 4/2, renovated, 0.8 mi

A sample output (illustrative): the renovated comps come to $209.60, $185.07, $193.57 and $175.15 per sq ft. Comp 5 is flagged as an outlier on size and bedrooms. Comp 4 is excluded from ARV because it is in dated condition, and used instead as a check on "as-is" value. The assistant gives an ARV range of $240,000 to $265,000 using the three closest renovated comps. Verify the sums: 262,000 divided by 1,250 is 209.6, correct, and 248,000 divided by 1,340 is 185.07, correct.

Now the offer sum, using the common 70% rule as a starting rule of thumb (not a standard; local margins differ). Take the mid ARV of $252,000. At 70%, that is $176,400. Subtract repairs. Suppose repairs are estimated at $38,000. The maximum allowable offer is $138,400 before your fee. If you want a $10,000 wholesale fee and the end buyer needs to keep their margin, the contract price you can offer is about $128,400 for a buyer who pays $138,400. Against an asking price of $190,000, the seller would have to come down by over $60,000. The AI has told you in fifteen minutes that this deal almost certainly will not work at the asking price, which is the answer you wanted before spending an hour on the phone. Of course, motivation may change the price; the arithmetic tells you what price makes it work.

Comps: what AI can and cannot do with them

An assistant can tidy a list of comps, compute price per square foot, flag outliers and suggest adjustments. It cannot check that a sale was arm's length, that the listed condition is true, or that the figures were typed correctly. A comp that sold to a relative at a discount, or a figure copied wrongly, flows straight into your ARV.

• Check each sale on a second source. Public records or listing data for price and date.
• Look at the photos. "Renovated" in a listing can mean new paint or a full gut. Ask the assistant for a one-line condition note per comp and compare it with the photos.
• Prefer recent, close and similar. A sale six months ago across a major road is a weak comp, whatever the table says.
• Beware a single comp driving the ARV. If the answer depends on one sale, find more evidence or widen the range.

For a broader view of how comps preparation works and where its limits sit, comps prep and draft report checks sets out the appraiser's approach, and a licensed agent or appraiser should confirm value when the deal is large or the comps are thin.

Repair estimates: category ranges with a contingency

This is where AI is most tempting and most dangerous, because costs vary by location and a model has no knowledge of your local contractors. Use it for structure, not for prices.

I am screening a wholesale deal. Based on the seller's notes and the photo descriptions below, list repair categories (roof, kitchen, bathrooms, flooring, paint, HVAC, electrical, plumbing, windows, exterior, landscaping, permits, cleanout), mark each as Likely / Possible / Unknown, and give the question I should ask the seller or contractor to resolve it. Do NOT give prices. I will add local prices.

NOTES: roof "about 20 years old", kitchen "dated, works", one bathroom "needs updating", carpets throughout, furnace "old but runs", no known leaks, tenant leaving, garage full of belongings.

A realistic sample: roof "Likely: age suggests replacement soon; ask for photos of the ridge and any stains in the attic"; furnace "Possible: ask for the age on the plate"; electrical "Unknown: ask whether the panel has been upgraded"; cleanout "Likely: garage contents". Now you add prices from your local contractor quotes or your own recent jobs, using a low, mid and high figure. Illustrative: roof $9,000 to $14,000, kitchen $12,000 to $20,000, bathroom $4,000 to $8,000, flooring and paint $8,000 to $12,000, furnace $0 to $5,500, cleanout $800 to $1,500. That puts the low total at $33,800 and the high at $61,000; taking the middle of each range gives a mid total of about $47,400.

The point of the low-mid-high spread is to see how much the deal's margin depends on the unknowns. If the deal still works at the high figure, you can offer with confidence. If it only works at the low, the roof and the furnace are your questions on the call. For the buyer's view of budgets, AI rehab budgets, comps and contractor quotes is the other half of this conversation.

A fixed template for the deal summary

Ask the assistant to fill a one-page summary so every deal looks the same and you can compare them.

Notice that the mid offer in the table, $129,000, uses the itemised mid repair figure, while the earlier example used a quick $38,000 estimate. That $9,400 gap comes straight off what you can offer. Whichever repair figure you use, state it, because a deal summary that does not show its assumptions cannot be challenged. Ask the assistant to recompute the offer at low, mid and high repairs and to show all three, so you see the range at once.

What you must still check by hand

• Title and liens. Ownership, mortgages, tax liens, judgments, probate. A title company or lawyer does this; AI cannot.
• Occupancy and access. Is the tenant leaving, and when? What does the lease say?
• Condition on the ground. Photos lie by omission. A walk-through, even a quick one by a partner or contractor, beats any estimate.
• Local rules on wholesaling. Some states regulate how you may market contracts you do not own, what you must disclose, and whether a licence is needed. Verify with the state regulator or a lawyer.
• Assignment terms. Whether your contract can be assigned, and what it must say about your role and your fee.
• Buyer's numbers. Your cash buyer's own repair and resale assumptions, since they decide what they will pay.

A realistic mistake: a wholesaler used an assistant-generated ARV of $270,000 based on comps that included a house with a finished basement the subject did not have. The deal went under contract, the buyer's own review put ARV at $245,000, and the buyer withdrew. The error was in the comps, not the arithmetic. The fix was a rule that every comp gets a one-line comparison with the subject, with differences listed, before it counts.

Seller outreach: AI drafts, you stay within the rules

An assistant can draft respectful, plain messages. What it cannot do is decide who you may contact, how, or when. Calls and text messages to sellers are regulated in the US, including consent, do-not-call lists and opt-out handling, and state rules may add more. Under the federal rules, for example, prerecorded telemarketing calls and marketing texts sent with an autodialer need the person's prior consent, written consent for marketing texts, and anyone can opt out at any time in any reasonable way, even after consenting. The practical approach:

1. Ask a lawyer which channels and lists you may use and what each message must include.
2. Use AI for the wording only, inside those limits.
3. Identify yourself and your business clearly, and say you are a buyer or you market contracts, as your state requires.
4. Honour every opt-out immediately and keep a record.

Write a short, respectful message to a homeowner who has not contacted us before, from a real estate investor who buys homes as they are. Under 70 words. Say who I am, why I am writing, and that they can reply STOP to opt out. No pressure, no claims about their situation, no mention of distress. Use [first name] and [your name].

A sample output (illustrative): "Hello [first name], this is [your name]. I buy homes as they are, with no repairs needed. If you ever think about selling yours, I would be glad to talk. Reply STOP and I won't message again." Whether you may send even that depends on consent and list rules; the wording is the easy part. More on the lead and offer workflow, including what to automate, is in AI for wholesalers: seller leads, offers and buyer lists, and the consent side of calls is covered in voice agents and TCPA.

A second deal: the one the screening should kill

Illustrative. A seller offers a 1,000 sq ft two-bedroom for $120,000. Two renovated comps sold at $160,000 and $152,000, but both are 0.9 miles away on the other side of a rail line, and the only comp on the subject's street, in dated condition, sold for $118,000 four months ago. The assistant, given the three sales and asked to flag problems, would say something like: "The two renovated comps are far from the subject and across a barrier; the closest comp suggests an as-is value near $118,000; ARV evidence is thin." The 70% rule on a $156,000 mid ARV gives $109,200 before repairs, and with $30,000 of repairs the maximum offer is about $79,200, far below the $120,000 ask.

So the screening kills the deal in ten minutes, which is a good result. A wholesaler who skips this step spends an evening negotiating, gets the seller down to $100,000, and then cannot find a buyer. The value of the routine is mostly in the deals you do not chase. Keep a list of declined deals with the reason; if you see the same reason repeatedly, such as thin comps in an area, change where you source.

Questions to ask the seller, written before the call

The assistant is good at turning your unknowns list into a call script. Ask for questions in a sensible order, easiest first, and keep them neutral.

• "How long have you owned the property, and is it your main home?" Opens the conversation and tells you about equity.
• "Is anyone living there now, and when will it be empty?" Occupancy decides timing.
• "Roughly how old are the roof, the furnace and the water heater?" Feeds the repair categories.
• "Have there been any leaks, repairs or insurance claims?" Hidden cost and history.
• "Is there a mortgage or any other loan on it?" A first look at the lien question, to be confirmed on title.
• "Are others on the title who would need to sign?" Surfaces probate and co-owner problems early.
• "What price would make this work for you, and what is your timeline?" The number and the motivation, in that order.

Print the list, take notes, and paste your notes back into the assistant after the call. Ask it to update the deal summary, to mark which unknowns are now answered, and to flag any answer that conflicts with what you thought before. Check the updated summary against your notes rather than trusting it: an assistant sometimes silently overwrites an unknown with a guess.

Buyer side: test whether your number will sell

A deal you cannot assign is not a deal. Before you sign with the seller, run the numbers from the end buyer's side, because a buyer thinks about margin differently from you. Suppose a cash buyer wants a $30,000 profit on a $252,000 resale after $47,400 of repairs, $12,000 of holding and selling costs and a $10,000 wholesale fee. The buyer's maximum purchase price is 252,000 minus 47,400 minus 12,000 minus 30,000, which is $162,600, and that already includes your fee, so the contract price you can sign at is $152,600. If the seller accepts that or less, the deal works; if the seller wants $170,000, it does not, however much you like the house.

Notice that the $152,600 contract price is well above the $119,000 the 70% rule gave. The rule builds in a generous allowance for costs and profit; a buyer whose real costs and margin are lower can pay more. That is why the rule is a screen for deciding whether to call, while your buyers' actual numbers set the price you sign at. If the two disagree, trust the buyer's numbers only when they come from deals the buyer has closed.

Give the assistant this structure and your buyers' real margins, taken from deals they have closed with you, and have it produce the buyer-side arithmetic as a table with three scenarios. Do not let it assume a margin; the number must come from your buyers. A buyer list you have built from real conversations is an asset no model can invent, and it is the reason a wholesaler's estimate can be better than a generic one. For automation around this step, see AI automations from sourcing to contracts.

A weekly routine with realistic time

Illustrative for a solo wholesaler screening ten leads a week.

That is about five and a half hours for ten leads, and it should cut the number you call from ten to perhaps three or four. If you screen more than you can verify, the verification is what slips, so cap the number of leads to the number you can check properly. Wider thinking on what to hand to a person and what to hand to AI is in virtual assistant or AI: what goes where.

Keeping score so the screening gets better

After ten deals, review how your screening compared with reality. Make a simple log: predicted ARV, final sale price, predicted repairs, actual repairs, and the deal outcome. If your ARVs come in 5% high on average, adjust your range. If repair estimates miss on roofs, add a roof question to the call script. That log, not the assistant, is what makes your screening sharper over time. You can paste the log into the assistant and ask what patterns it sees, then check those patterns against your own memory of the deals.

A tool plan for a solo wholesaler: one assistant plan at about $20 a month (Claude Pro is $20 a month, $17 billed annually, on Anthropic's pricing page in November 2026), plus a spreadsheet. Add paid lead lists, dialers and CRMs only when screening is working and the bottleneck is volume. If you would rather build the routine with someone, that is what my AI implementation consultation covers, from the first screening checklist to deciding which steps to automate.

Questions wholesalers ask about AI underwriting

Can AI calculate ARV for me?

It can organise comps and do the arithmetic, but it cannot verify that comps are genuine, recent or comparable. Treat any ARV it produces as a draft. Confirm the sales on a records source or listing data, check condition and layout, and have an agent or appraiser confirm when the deal is large.

Which AI is best for wholesaling?

A general assistant such as ChatGPT or Claude is enough to start, with a spreadsheet for the numbers. Specialist wholesaling software bundles lead lists and dialers, which is a different job from underwriting. Test any bundle on a few of your own deals before paying.

Do I need a licence to wholesale?

It depends on the state and on how you structure the deal. Some states require a licence for marketing contracts you do not own, or have rules on disclosure and assignment. Check your state's current rules with the real estate regulator or a local lawyer before you market any deal.

Can I send AI-written texts and emails to sellers?

The rules on calls, texts and email apply whoever writes the message, including consent, opt-outs and do-not-call lists. Have a lawyer confirm what you may send, to whom and how, and make sure every message identifies who you are and gives a way to stop.

Further reads

• AI Automation Agency for Real Estate: Scope, Fees and Red Flags — What an automation agency scope and fees look like.
• Can AI Prepare a CMA? What Realtors Must Still Verify — How a CMA is prepared and what must still be verified.
• AI Due Diligence on Real Estate: Read Leases, Surveys, Searches — Due diligence checks on a purchase.
• Real Estate Chatbot Pricing: What Agents Pay for AI Lead Capture — What agents pay for AI lead capture.
• How to Automate Buyer and Vendor Follow-Up With AI — Follow-up that keeps sellers and buyers warm.
• AI Commercial Real Estate Underwriting: A First-Pass Model — A first-pass model for larger deals.
• AI Property Development Feasibility: A First-Pass Appraisal — A worked first-pass appraisal for a small developer: inputs, GDV, costs, sensitivity and planning risks, with AI as challenger and a spreadsheet as calculator.
• AI Tools and AI Development: The Complete 2026 Guide — the AI hub, including every tutorial in the AI-for-business series.

Sources: Claude pricing page (November 2026); FCC consumer guide on unwanted robocalls and texts (November 2026). The 70% rule is a widely used rule of thumb, not a legal or valuation standard. State licensing and outreach rules must be checked locally.