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Apex Deal Room

Know what the deal can really support — before you put it under contract.

For every lead, or address you type, Deal Room pulls the comps, builds the ARV, calculates the cost of repairs (when there are walkthrough photos of the property), runs the analysis, shows you what to offer the seller, what you stand to make, and which buyers have actually been buying deals like this one — before you commit.

The Deal Room below is the actual product. Change the numbers, open the analysis and work the deal yourself.

The five questions every deal demands, answered on page load.

1What’s it worth?Your ARV, built from the comps and checked against independent valuations — with the buyer’s break-even if the exit lands lower.
2What will it cost?The repair scope priced room by room from the walkthrough photos, driving the deal instead of sitting beside it.
3What can I offer?The traditional MAO, and next to it an offer worked backward from what the buyer actually needs to make.
4How much can I make?The assignment fee this deal can carry — and the point where asking for more starts costing you buyers.
5Who will buy it?The buyers who can take this property at this price, ranked on what they say they buy and what they have actually bought.
What is Deal Room?

Analyze the property. Structure the deal. Know whether it actually works.

Deal Room is where you take a property from address to offer. The property record and comps, your ARV, the repair scope, the seller and their conversation, your offer, your assignment fee and the buyers who would have to purchase it — all on the same Deal Room, working against the same live property.

So you are not bouncing between property-data sites, comp tools, repair estimates, spreadsheets, your CRM and your buyer list trying to piece together whether the deal works.

And because it is all working on the same deal, the pieces do not stay isolated. Change the repairs and see what happens to the offer, the buyer’s return and your profit. Change your assignment fee and see which buyers disappear. Change the ARV and see whether the deal still holds up.

Everything in one place is the feature. Everything working together is what makes the difference.

Before you commit

Find out with certainty whether the deal works — while you can still walk away.

You pull the property. Find comps. Choose an ARV. Estimate repairs. Run a formula. Make an offer. Decide what you want to make.

Then you put the property under contract and find out whether your buyers agree.

Deal Room brings the buyer into the math before you get there.

Deal Room — walkthrough A deal worked end to end

Watch a deal worked end to end — then scroll down and work one yourself in the real application below.

Deal Room — live Real application — click into it

Everything further down this page is a still of one panel from the room above. Up here it all works.

ARV + valuation

A deal is only as good as the ARV holding it together.

Push the ARV high enough and almost any deal looks good.

Deal Room makes you defend it.

Build your ARV from the comps you selected. Compare it against available independent valuation data. See the price per square foot. See when your ARV sits above another valuation range.

Then take the more important step: see what happens to the buyer if your ARV is wrong.

  • Build the ARV from comps. — Choose the sales you trust instead of treating every nearby property as equally relevant.
  • See when you’re stretching. — If your ARV is significantly above another valuation range, Deal Room makes that assumption visible.
  • Test a lower exit. — See the buyer’s profit and return if the property sells for less than your ARV.
  • Know the break-even. — See how far the resale price can fall before the buyer’s profit disappears.

Don’t just find the number that makes the deal work. Find out how much of the deal depends on that number being right.

412 Whitmore St · Bellaire TX
Provided via the Zestimate API
$179 / sqft
Zestimate® home valuation
$264,900
+$3,099 (+1.2%) over 30 days
$47k cushion $19k premium $249k $281k Break-even $201,887 Zestimate $264,900 Your ARV $300,000 $187k $315k
Purple = where Zillow thinks value lands.
ConfidenceZestimate API
Forecast std deviation6.0%
Confidence band$249,006 – $280,794
Value 30 days ago$261,801
ZPID27841903 · 2d ago
Offer benchmarksComputed
70% rule max offer$155,430
MAO ÷ Zestimate68.0%
Equity vs Zestimate$176,500
Buyer profit at exitComputed
$47k $63k $79k $98k Band low Zestimate Band high Your ARV 23.3% ROI 31.2% ROI 39.1% ROI 48.6% ROI
This deal against ZillowComputed
Break-even resale$201,887offer + fee + repairs + costs
Value can fall23.8%before the buyer breaks even
Spread at Zestimate$63,01323.8% of value
All-in ÷ Zestimate73.6%under the 75% rule
All-in ÷ band low78.3%worst-case ratio
Your ARV vs Zestimate+$35,10013.3% — above the band
Provided via the Zestimate API
$25,800 / yr
Rent Zestimate home valuation
$2,150 / mo
vs Zestimate 0.81% vs all-in 1.10% 1% rule
Income metricsComputed
Gross yield · Zestimate9.7%$25,800 ÷ $264,900
Gross yield · all-in13.2%$25,800 ÷ $195,000
Cap rate · Zestimate4.9%50% expense rule
Cap rate · all-in6.6%50% expense rule
Estimated NOI$12,900$1,075 per month
Cash on cash6.4%on $201,887 all-in
Gross rent multiplier10.3lower is better
Break-even rent$1,439per month
Your ARV is $19,206 above the top of Zillow’s band. Even at the band low the buyer clears $47k, so the deal survives — the premium is what to defend.
Live from Bridge zestimates_v2, displayed only — excluded from Save analysis model and Push to repository. Cap rates assume 50% expenses.

Purple is where Zillow thinks value lands. The cushion and premium brackets are computed against your own numbers.

EstiMate

Build the deal around the real repair number.

A bad repair number can destroy an otherwise good deal.

When an EstiMate scope exists for the property, Deal Room can bring the actual scope into the analysis — the rooms, the repair lines and the rehab total.

Choose the appropriate repair level and that number becomes part of the economics of the deal.

Now repairs are not sitting in a separate estimate. They affect what you can offer. They affect what the buyer has invested. They affect the buyer’s return. They affect which buyers still fit. And they affect how much room remains for your assignment fee.

  • Room-by-room scope — Use the EstiMate project created from the property walkthrough instead of relying on one rough rehab number.
  • Multiple repair levels — Compare Economy, Standard and High-End repair totals without rebuilding the scope.
  • Working or committed — See whether the estimate is still being worked or has been committed as the current scope.
  • Know when it changes — If the linked scope changes after you priced the deal, Deal Room can surface the updated scope instead of quietly changing the assumption underneath you.

When the rehab changes, the deal changes.

412 Whitmore St · Bellaire TX
ScopeCreatedRoomsRepairsStatus
Same addressWhitmore St — full gut412 Whitmore St, Bellaire TX 77401 · R. Whitmore · 3 bd · 2 ba · 1,480 sqftJul 11, 26updated 13d ago647 lines$38,400standard
No dealPhone scan — kitchen + baths412 Whitmore St, Bellaire TX 77401 · 3 bd · 2 ba · 1,480 sqftJul 21, 26updated 20d ago319 lines$21,750standard
Ridgeway Ave — cosmetic88 Ridgeway Ave, Houston TX 77008 · M. Okafor · 2 bd · 1 ba · 1,120 sqftJun 28, 26updated Jul 2, 26422 lines$16,900standard
Cedar Bayou duplex — roof + HVAC1207 Cedar Bayou Rd, Baytown TX 77520 · Pareja LLC · 4 bd · 3 ba · 2,240 sqftJun 4, 26updated Jun 19, 26861 lines$52,300standard
4 scopes · 2 still being scoped Committed means the scoper is done experimenting and stands behind the numbers — togglable from either app.

Every scope on the account, searchable by address, client or project ID.

Work the numbers

Change one number. See who pays for it.

ARV. Seller offer. Repairs. Assignment fee.

Those numbers are fighting over the same deal.

Give the seller more and there is less room somewhere else. Repairs go up and somebody loses margin. Increase your assignment fee and the buyer pays a higher contract price. Lower the ARV and the buyer has less profit waiting at the exit.

Deal Room recalculates the economics as those numbers move.

So instead of asking whether one number looks good by itself, you can see what that number does to everyone else in the deal.

OfferAid™

Work backward from a deal the buyer can actually buy.

Deal Room still gives you the traditional MAO — 70% of ARV minus repairs — as the familiar benchmark. But a rule of thumb does not know what a particular buyer requires, how many buyers can support the price, or whether your assignment fee pushes the buyer’s economics too far.

MAO starts with a formula. OfferAid starts with the economics the deal ultimately has to survive.

It looks at the ARV, repairs, holding costs, closing costs, your assignment fee and the return left for the buyer. Then it works backward to the seller offer.

That means you are not simply asking “what does the 70% rule say I can pay?” You can ask “what can I pay the seller while this still works for the people who have to buy it from me?”

Then OfferAid lets you compare different ways of structuring the opportunity.

  • Fast Sale — Put more of the available room toward the seller and create a lower price for the buyer. You keep less. More buyers may be able to take the deal.
  • Highest Profit — Push more of the available room toward your assignment fee. You make more if it closes, but the higher buyer price can reduce the number of buyers who can take it.
  • Buyer Match — Protect more of the buyer economics and prioritize a structure with stronger buyer compatibility.
  • Balanced — Split the available room between the seller, your fee and the buyer rather than pushing the deal entirely toward one side.
412 Whitmore St · Bellaire TX
Deal parametersmatches the deal
ARV$300,000
40%
Seller offer$140,000
40%
Repair cost$30,000
40%
Assignment fee$25,000
40%
Optimization strategy
Resistance limits how far OfferAid may move each number. Lock one at 100% and it optimises around it.
Mode comparisonClick a row to apply
ModeOfferFeeROIDAPBuyers
Fast sale$140,000$069.6%76%189
Custom$140,000$25,00048.6%72%169
Match$140,000$25,00048.6%72%169
Balanced$160,500$45,50023.5%44%104
Profit$140,000$87,00013.7%28%28
Trade-off
Highest profit pays you $87,000 more than Fast sale, but Fast sale reaches 161 more buyers (189 vs 28) and 48% more acceptance.
Warnings & insights
CautionRepair cost is $20/sqft on a 1968 build. Verify before committing.
CautionARV of $300,000 is above Zillow’s band top of $280,794.
SuggestionTop fit is Grant Alvarez. Start there.
InfoMoney pot is ARV ÷ 1.15 less repairs, holding and closing. Each mode splits it differently.
CriticalNo facilitator pool selected — all 200 buyers are in scope.
Optimized structureCustom
ARV$300,000
Seller offer$140,000
Repair cost$30,000
Assignment fee$25,000
Holding cost$3,969
Closing costs$2,918
Buyer ROI48.6%
Deal acceptance72%
Buyers matched169
Contract price to buyer$165,000
Money pot
Pot at a 15% buyer return$223,983
Allocated to the seller62.5%
Allocated to you11.2%

Every strategy is scored against the same buyer pool, so the comparison is like for like.

But real deals aren’t completely flexible.

Maybe the ARV is still a rough estimate. Maybe an agent gave you the number. Maybe you have an appraisal.

Maybe repairs are still rough. Maybe you walked the property. Maybe a contractor already priced the job.

Maybe the seller is still negotiating. Maybe they just gave you their final number.

OfferAid accounts for how much freedom actually remains in those numbers.

The more established a number becomes, the less freedom the system has to move it. Lock something down and OfferAid has to find the answer somewhere else.

Don’t ask for one magic offer. See the different ways the deal can actually work.

412 Whitmore St · Bellaire TX
Deal parametersmatches the deal
ARV$300,000
40%
Seller offer$140,000
40%
Repair cost$30,000
40%
Assignment fee$25,000
40%
Optimization strategy
Resistance limits how far OfferAid may move each number. Lock one at 100% and it optimises around it.
Tell Deal Room what you actually know

A guess shouldn’t carry the same weight as a fact.

If an appraiser gave you the ARV, that number is solid. If you estimated it in the car on the way over, it is not. If a contractor priced the repairs, that number is solid. If you used $20 a foot, it is not. If the seller already gave you their final number, there is no room left. If you have not talked price yet, there is plenty.

You are not asked to tune an optimiser. You are simply asked to describe your current situation, so the offer OfferAid builds is one you can actually stand behind — aggressive where you have room, and careful where you do not.

Beside every value is a dropdown. Pick the one that describes your reality:

  • ARV — Exploring, Guessing, Agent or Appraisal.
  • Seller offer — Unrestricted, Negotiating, Countered or Finalizing.
  • Repair cost — Rough, Walkthrough, Contractor or Close.
  • Assignment fee — Fair, Balanced or Greedy.

Each choice sets how much freedom OfferAid has to move that number when it structures the offer. The blue sliders on the left are the numbers themselves; the red ones on the right are how firm those numbers are.

The stronger the evidence behind a number, the less OfferAid tries to move it. Lock one down completely and it has to find the answer somewhere else in the deal.

MaxFee™

More assignment fee does not always mean more money.

You can put any assignment fee you want on a deal. That does not mean the market will pay it.

Every dollar you add to your fee increases the buyer’s price. Their return gets smaller. Some buyers reach their limit. Then another buyer reaches theirs. Then another.

Eventually the extra money you are trying to make starts making the deal harder to sell.

MaxFee shows you where that happens. It evaluates your assignment fee against the economics of the deal and the buyers who would actually have to take it.

  • Your profit — See what the current assignment fee pays you.
  • Buyer return — See the return left for the buyer after purchase price, repairs, holding and closing costs.
  • Buyers remaining — See how many buyers can still support the deal at the current structure.
  • Buyer drop-off — As your fee increases, see buyers disappear when the numbers no longer meet their individual economics.
  • Optimal fee — See where the model finds the strongest balance between what you make on the deal and the likelihood of actually getting that fee.
  • The cliff — See where buyer acceptance becomes weak enough that pushing harder can work against you.
  • Your headroom — See how far your current fee sits from that danger point.

MaxFee answers the question every wholesaler eventually runs into: how much can I make before asking for more starts making the deal harder to sell?

412 Whitmore St · Bellaire TX
Acceptance72%
deal acceptance probability
1007550250DAP %cliff72%
Buyers matched168 of 200
buy-box still admits the deal at $201,887 all-in
200150100500BUYERSoptimal168$0$25k$50k$75k$100kASSIGNMENT FEE ($)
Your profit$25,000at this fee
Buyers matched168of 200 in pool
Buyer ROI48.6%above 15% floor
DAP at fee72%acceptance
Fee position
82
Deal health / 100
$0 optimal $55k $100k
Insights
SoundBuyer ROI 48.6% against a 15% floor.
Off peak$12,582 of expected value left on the table — the peak is $55,000.
Room$25,143 below the acceptance cliff.
Market defaults · tier 0
Readouts
Assignment fee$25,000
Optimal fee$55,000
Cliff at$50,143
Headroom$25,143
Buyer all-in$195,000
All-in / ARV65.0%
Fee as % of ARV8.3%
Peer floor$15,000
Tolerance ceiling$83,983
Expected value$17,935
EV at optimal$30,516
Controls removed — the HUD calculator is the only input. No facilitator pool selected, so counts run on market defaults.

The real MaxFee panel. Drag either chart in the product and the fee scrubs with it; the dashed red line is the cliff.

Buyer Match

Count the buyers who can actually buy this deal.

A buyer being in your database means almost nothing.

The question is whether this property works for that buyer at the price you are creating.

Deal Room evaluates the deal buyer by buyer.

For each buyer, it can look at the price they can support, the return they require, the rehab they will tolerate, the location and how much room remains between your current price and their limit.

  • Maximum offer — See the modeled maximum price each buyer can support while preserving their required economics.
  • Headroom — See how much room remains between your current contract price and that buyer’s limit.
  • Buyer ROI — See whether enough return remains for that buyer.
  • Rehab fit — See whether the repair level fits what they are prepared to take on.
  • Location fit — See how well the property fits where they actually buy.

Buyer demand becomes part of underwriting the deal — not something you discover after you already own the contract.

412 Whitmore St · Bellaire TX
Buyers matched169
Highest fit score64
Deal-level DAP72%
0 score 70+, 72 verified · median headroom $48,280 over your $165,000 price · 0 could pay but want a better return · no facilitator pool selected
Buyer radardrag to select
0%$014%$60k28%$119k42%$179k56%$239kREQUIRED RETURNMAX OFFERyour price $165kyour deal returns 49%Fiona Alvarez — could pay $129,444, needs 24.6%Grant Petrov — could pay $175,974, needs 26.9%Andre Raman — could pay $189,559, needs 19.3%Grant Kowalski — could pay $198,357, needs 25.1%Tomas Okonkwo — could pay $223,690, needs 15.0%Hassan Bryant — could pay $97,202, needs 23.8%Simone Foster — could pay $226,441, needs 12.4%Curtis Vaughn — could pay $213,023, needs 20.3%Simone Lindgren — could pay $166,126, needs 10.1%Andre Reyes — could pay $222,691, needs 17.0%Grant Foster — could pay $215,979, needs 19.4%Wes Hart — could pay $220,478, needs 17.7%Nadia Vaughn — could pay $220,135, needs 16.6%Wes Haddad — could pay $175,548, needs 22.8%Cora Webb — could pay $201,816, needs 26.3%Dmitri Petrov — could pay $218,106, needs 18.7%Dmitri Hart — could pay $218,425, needs 18.5%Curtis Foster — could pay $224,498, needs 15.0%Yara Petrov — could pay $201,872, needs 26.8%Marcus Reyes — could pay $214,612, needs 15.8%Grant Webb — could pay $161,288, needs 11.9%Andre Raman — could pay $206,418, needs 24.7%Priya Bryant — could pay $214,801, needs 19.4%Tomas Vaughn — could pay $140,297, needs 23.8%Simone Webb — could pay $201,225, needs 13.9%Simone Hart — could pay $178,086, needs 13.2%Wes Hart — could pay $134,863, needs 19.5%Curtis Gallagher — could pay $141,638, needs 26.4%Ruben Vaughn — could pay $216,470, needs 10.3%Yara Okonkwo — could pay $216,333, needs 19.0%Dmitri Okonkwo — could pay $199,634, needs 27.5%Owen Petrov — could pay $196,538, needs 27.9%Dmitri Bryant — could pay $132,770, needs 15.9%Priya Moss — could pay $215,139, needs 20.3%Owen Bryant — could pay $201,368, needs 26.9%Grant Lindgren — could pay $216,124, needs 19.7%Owen Okonkwo — could pay $235,640, needs 10.7%Owen Haddad — could pay $176,234, needs 17.2%Andre Petrov — could pay $193,020, needs 21.1%Wes Okonkwo — could pay $201,912, needs 24.7%Hassan Hart — could pay $228,342, needs 14.3%Ruben Bell — could pay $234,863, needs 11.4%Yara Moss — could pay $232,624, needs 11.6%Iris Hart — could pay $174,636, needs 11.6%Dmitri Webb — could pay $218,114, needs 19.2%Hassan Gallagher — could pay $170,002, needs 16.2%Grant Lindgren — could pay $104,038, needs 16.4%Dmitri Vaughn — could pay $118,992, needs 20.4%Cora Okonkwo — could pay $181,384, needs 12.4%Nadia Webb — could pay $230,806, needs 12.1%Lena Gallagher — could pay $178,923, needs 10.5%Owen Nakamura — could pay $207,480, needs 23.3%Curtis Bryant — could pay $128,407, needs 25.9%Iris Bryant — could pay $229,141, needs 13.8%Hassan Bryant — could pay $203,652, needs 25.7%Dmitri Kowalski — could pay $210,082, needs 11.1%Owen Gallagher — could pay $147,490, needs 26.3%Owen Duval — could pay $213,923, needs 20.2%Yara Sayed — could pay $202,865, needs 25.1%Cora Kowalski — could pay $216,860, needs 19.0%Nadia Ortiz — could pay $209,618, needs 22.6%Simone Hart — could pay $202,312, needs 19.7%Yara Bell — could pay $203,329, needs 26.7%Dmitri Petrov — could pay $221,548, needs 17.3%Simone Foster — could pay $155,686, needs 12.2%Curtis Petrov — could pay $212,218, needs 21.5%Nadia Gallagher — could pay $212,019, needs 21.1%Devon Hart — could pay $219,500, needs 17.8%Wes Kowalski — could pay $218,195, needs 18.6%Marcus Vaughn — could pay $216,151, needs 19.1%Dmitri Kowalski — could pay $208,534, needs 23.3%Owen Duval — could pay $171,423, needs 19.0%Tomas Foster — could pay $213,296, needs 21.5%Tomas Duval — could pay $221,262, needs 10.7%Andre Haddad — could pay $229,635, needs 13.6%Curtis Haddad — could pay $200,294, needs 27.7%Iris Bryant — could pay $204,686, needs 25.5%Iris Sayed — could pay $158,258, needs 18.1%Andre Vaughn — could pay $171,568, needs 25.2%Grant Duval — could pay $183,336, needs 24.0%Yara Vaughn — could pay $223,053, needs 16.2%Yara Hart — could pay $230,158, needs 13.1%Owen Sayed — could pay $229,778, needs 13.2%Curtis Haddad — could pay $214,072, needs 20.5%Hassan Ortiz — could pay $214,876, needs 20.5%Tomas Reyes — could pay $222,804, needs 16.1%Ruben Foster — could pay $155,607, needs 27.4%Tomas Hart — could pay $205,696, needs 25.1%Andre Haddad — could pay $141,422, needs 19.2%Simone Bryant — could pay $234,974, needs 11.6%Simone Haddad — could pay $232,400, needs 11.9%Hassan Lindgren — could pay $238,007, needs 10.2%Cora Ortiz — could pay $159,698, needs 16.3%Marcus Petrov — could pay $200,353, needs 27.8%Elena Duval — could pay $201,759, needs 26.1%Hassan Gallagher — could pay $228,627, needs 13.5%Grant Alvarez — could pay $221,855, needs 16.3%Owen Vaughn — could pay $205,158, needs 13.3%Nadia Moss — could pay $151,828, needs 23.3%Wes Moss — could pay $214,716, needs 20.7%Hassan Alvarez — could pay $217,077, needs 18.2%Dmitri Reyes — could pay $221,502, needs 16.9%Ruben Gallagher — could pay $205,473, needs 24.7%Priya Duval — could pay $207,285, needs 24.3%Ruben Foster — could pay $204,474, needs 25.0%Hassan Reyes — could pay $140,309, needs 13.5%Iris Bryant — could pay $168,642, needs 17.2%Simone Hart — could pay $202,457, needs 12.7%Yara Raman — could pay $155,291, needs 21.9%Ruben Nakamura — could pay $192,068, needs 11.2%Wes Foster — could pay $161,436, needs 14.8%Fiona Kowalski — could pay $200,256, needs 27.7%Priya Gallagher — could pay $200,421, needs 26.8%Elena Webb — could pay $225,743, needs 15.4%Elena Kowalski — could pay $171,792, needs 13.4%Dmitri Bryant — could pay $227,025, needs 14.5%Andre Gallagher — could pay $212,566, needs 14.8%Yara Moss — could pay $232,780, needs 11.2%Nadia Vaughn — could pay $140,817, needs 18.8%Ruben Bell — could pay $229,985, needs 12.5%Grant Okonkwo — could pay $222,300, needs 16.1%Lena Kowalski — could pay $222,629, needs 16.5%Curtis Gallagher — could pay $178,102, needs 14.4%Hassan Sayed — could pay $221,904, needs 16.3%Yara Gallagher — could pay $197,226, needs 27.9%Elena Raman — could pay $222,995, needs 16.7%Devon Vaughn — could pay $210,774, needs 22.6%Ruben Alvarez — could pay $193,946, needs 11.6%Elena Haddad — could pay $235,411, needs 10.2%Devon Moss — could pay $226,155, needs 14.3%Elena Lindgren — could pay $121,438, needs 25.6%Iris Ortiz — could pay $163,847, needs 22.4%Simone Gallagher — could pay $225,319, needs 15.6%Elena Vaughn — could pay $230,127, needs 13.2%Iris Alvarez — could pay $174,934, needs 16.4%Fiona Kowalski — could pay $221,717, needs 16.5%Elena Reyes — could pay $155,943, needs 21.0%Simone Moss — could pay $238,289, needs 10.0%Hassan Foster — could pay $215,099, needs 20.0%Hassan Haddad — could pay $213,534, needs 21.1%Curtis Kowalski — could pay $200,169, needs 27.4%Tomas Haddad — could pay $185,222, needs 27.9%Lena Raman — could pay $207,615, needs 19.8%Elena Ortiz — could pay $217,509, needs 18.3%Fiona Lindgren — could pay $173,552, needs 24.0%Curtis Raman — could pay $221,646, needs 16.5%Priya Raman — could pay $198,910, needs 27.4%Marcus Reyes — could pay $200,276, needs 26.5%Owen Raman — could pay $210,873, needs 20.9%Cora Haddad — could pay $183,384, needs 26.3%Iris Moss — could pay $233,089, needs 12.3%Simone Moss — could pay $200,178, needs 27.4%Curtis Bell — could pay $197,527, needs 20.2%Fiona Alvarez — could pay $211,030, needs 21.9%Simone Reyes — could pay $237,134, needs 10.5%Lena Gallagher — could pay $176,166, needs 22.2%Cora Bryant — could pay $228,022, needs 14.1%Lena Sayed — could pay $224,578, needs 16.2%Ruben Gallagher — could pay $204,889, needs 25.6%Dmitri Ortiz — could pay $162,717, needs 22.4%Simone Bryant — could pay $222,382, needs 17.0%Grant Okonkwo — could pay $218,316, needs 18.3%Owen Alvarez — could pay $225,149, needs 14.7%Elena Nakamura — could pay $216,282, needs 19.0%Curtis Bryant — could pay $139,270, needs 10.1%Elena Kowalski — could pay $204,985, needs 25.5%Owen Raman — could pay $222,102, needs 16.6%Andre Raman — could pay $229,468, needs 13.6%Iris Raman — could pay $226,947, needs 14.1%Owen Vaughn — could pay $129,147, needs 11.5%Simone Webb — could pay $92,532, needs 22.3%Dmitri Raman — could pay $217,871, needs 19.1%Nadia Duval — could pay $170,027, needs 22.0%Hassan Gallagher — could pay $213,280, needs 21.2%Grant Lindgren — could pay $233,099, needs 11.3%Cora Ortiz — could pay $191,792, needs 22.9%Cora Moss — could pay $218,284, needs 18.3%Elena Bryant — could pay $228,076, needs 12.9%Tomas Alvarez — could pay $186,426, needs 23.2%Ruben Hart — could pay $180,015, needs 10.9%Simone Sayed — could pay $217,461, needs 18.5%Wes Bryant — could pay $210,244, needs 22.7%Iris Bell — could pay $204,390, needs 25.5%Tomas Bryant — could pay $212,077, needs 22.0%Yara Alvarez — could pay $206,492, needs 24.9%Lena Reyes — could pay $186,849, needs 18.4%Fiona Lindgren — could pay $182,478, needs 14.0%Owen Ortiz — could pay $205,277, needs 21.0%Tomas Alvarez — could pay $197,826, needs 21.2%Marcus Haddad — could pay $235,979, needs 11.1%Cora Webb — could pay $144,352, needs 16.2%Marcus Foster — could pay $238,511, needs 10.0%Priya Kowalski — could pay $149,970, needs 17.9%Marcus Lindgren — could pay $207,264, needs 24.1%Tomas Petrov — could pay $215,840, needs 19.2%Tomas Ortiz — could pay $207,794, needs 24.1%Grant Alvarez — could pay $162,438, needs 11.3%Priya Reyes — could pay $237,083, needs 10.2%Priya Gallagher — could pay $169,262, needs 23.6%Lena Duval — could pay $215,759, needs 19.9%
Match quality
0%0%25%25%50%50%75%75%100%100%SMARTMATCHBUY BOX MATCHTRUE FIT 96HIDDEN 18ASPIRATIONAL 53POOR FIT 33Fiona Alvarez — BBM 40%, SmartMatch 35%Grant Petrov — BBM 54%, SmartMatch 45%Andre Raman — BBM 63%, SmartMatch 74%Grant Kowalski — BBM 46%, SmartMatch 59%Tomas Okonkwo — BBM 42%, SmartMatch 4%Hassan Bryant — BBM 53%, SmartMatch 16%Simone Foster — BBM 52%, SmartMatch 64%Curtis Vaughn — BBM 47%, SmartMatch 46%Simone Lindgren — BBM 49%, SmartMatch 55%Andre Reyes — BBM 58%, SmartMatch 67%Grant Foster — BBM 67%, SmartMatch 54%Wes Hart — BBM 68%, SmartMatch 63%Nadia Vaughn — BBM 73%, SmartMatch 71%Wes Haddad — BBM 64%, SmartMatch 48%Cora Webb — BBM 68%, SmartMatch 57%Dmitri Petrov — BBM 50%, SmartMatch 11%Dmitri Hart — BBM 74%, SmartMatch 45%Curtis Foster — BBM 51%, SmartMatch 52%Yara Petrov — BBM 68%, SmartMatch 65%Marcus Reyes — BBM 62%, SmartMatch 66%Grant Webb — BBM 47%, SmartMatch 45%Andre Raman — BBM 59%, SmartMatch 61%Priya Bryant — BBM 59%, SmartMatch 47%Tomas Vaughn — BBM 53%, SmartMatch 46%Simone Webb — BBM 72%, SmartMatch 46%Simone Hart — BBM 47%, SmartMatch 69%Wes Hart — BBM 55%, SmartMatch 50%Curtis Gallagher — BBM 35%, SmartMatch 8%Ruben Vaughn — BBM 58%, SmartMatch 57%Yara Okonkwo — BBM 70%, SmartMatch 66%Dmitri Okonkwo — BBM 54%, SmartMatch 12%Owen Petrov — BBM 66%, SmartMatch 56%Dmitri Bryant — BBM 40%, SmartMatch 16%Priya Moss — BBM 78%, SmartMatch 43%Owen Bryant — BBM 67%, SmartMatch 63%Grant Lindgren — BBM 66%, SmartMatch 64%Owen Okonkwo — BBM 75%, SmartMatch 54%Owen Haddad — BBM 33%, SmartMatch 9%Andre Petrov — BBM 52%, SmartMatch 55%Wes Okonkwo — BBM 66%, SmartMatch 67%Hassan Hart — BBM 64%, SmartMatch 58%Ruben Bell — BBM 70%, SmartMatch 66%Yara Moss — BBM 48%, SmartMatch 5%Iris Hart — BBM 42%, SmartMatch 40%Dmitri Webb — BBM 63%, SmartMatch 62%Hassan Gallagher — BBM 39%, SmartMatch 10%Grant Lindgren — BBM 54%, SmartMatch 49%Dmitri Vaughn — BBM 40%, SmartMatch 60%Cora Okonkwo — BBM 62%, SmartMatch 59%Nadia Webb — BBM 53%, SmartMatch 3%Lena Gallagher — BBM 50%, SmartMatch 65%Owen Nakamura — BBM 68%, SmartMatch 69%Curtis Bryant — BBM 50%, SmartMatch 55%Iris Bryant — BBM 65%, SmartMatch 64%Hassan Bryant — BBM 60%, SmartMatch 69%Dmitri Kowalski — BBM 62%, SmartMatch 50%Owen Gallagher — BBM 43%, SmartMatch 72%Owen Duval — BBM 73%, SmartMatch 54%Yara Sayed — BBM 63%, SmartMatch 16%Cora Kowalski — BBM 60%, SmartMatch 38%Nadia Ortiz — BBM 56%, SmartMatch 10%Simone Hart — BBM 59%, SmartMatch 45%Yara Bell — BBM 43%, SmartMatch 1%Dmitri Petrov — BBM 77%, SmartMatch 53%Simone Foster — BBM 51%, SmartMatch 65%Curtis Petrov — BBM 62%, SmartMatch 18%Nadia Gallagher — BBM 71%, SmartMatch 67%Devon Hart — BBM 55%, SmartMatch 37%Wes Kowalski — BBM 54%, SmartMatch 21%Marcus Vaughn — BBM 62%, SmartMatch 48%Dmitri Kowalski — BBM 71%, SmartMatch 62%Owen Duval — BBM 57%, SmartMatch 60%Tomas Foster — BBM 74%, SmartMatch 57%Tomas Duval — BBM 57%, SmartMatch 76%Andre Haddad — BBM 57%, SmartMatch 52%Curtis Haddad — BBM 48%, SmartMatch 8%Iris Bryant — BBM 72%, SmartMatch 49%Iris Sayed — BBM 45%, SmartMatch 65%Andre Vaughn — BBM 43%, SmartMatch 54%Grant Duval — BBM 41%, SmartMatch 2%Yara Vaughn — BBM 45%, SmartMatch 57%Yara Hart — BBM 62%, SmartMatch 66%Owen Sayed — BBM 63%, SmartMatch 69%Curtis Haddad — BBM 57%, SmartMatch 4%Hassan Ortiz — BBM 67%, SmartMatch 60%Tomas Reyes — BBM 59%, SmartMatch 60%Ruben Foster — BBM 23%, SmartMatch 32%Tomas Hart — BBM 62%, SmartMatch 63%Andre Haddad — BBM 30%, SmartMatch 6%Simone Bryant — BBM 55%, SmartMatch 67%Simone Haddad — BBM 62%, SmartMatch 65%Hassan Lindgren — BBM 74%, SmartMatch 46%Cora Ortiz — BBM 25%, SmartMatch 3%Marcus Petrov — BBM 70%, SmartMatch 68%Elena Duval — BBM 54%, SmartMatch 58%Hassan Gallagher — BBM 52%, SmartMatch 49%Grant Alvarez — BBM 70%, SmartMatch 79%Owen Vaughn — BBM 50%, SmartMatch 18%Nadia Moss — BBM 41%, SmartMatch 27%Wes Moss — BBM 51%, SmartMatch 3%Hassan Alvarez — BBM 59%, SmartMatch 8%Dmitri Reyes — BBM 73%, SmartMatch 73%Ruben Gallagher — BBM 60%, SmartMatch 69%Priya Duval — BBM 69%, SmartMatch 58%Ruben Foster — BBM 62%, SmartMatch 28%Hassan Reyes — BBM 40%, SmartMatch 52%Iris Bryant — BBM 38%, SmartMatch 52%Simone Hart — BBM 55%, SmartMatch 44%Yara Raman — BBM 52%, SmartMatch 60%Ruben Nakamura — BBM 48%, SmartMatch 66%Wes Foster — BBM 36%, SmartMatch 8%Fiona Kowalski — BBM 48%, SmartMatch 60%Priya Gallagher — BBM 64%, SmartMatch 2%Elena Webb — BBM 61%, SmartMatch 59%Elena Kowalski — BBM 49%, SmartMatch 34%Dmitri Bryant — BBM 68%, SmartMatch 48%Andre Gallagher — BBM 52%, SmartMatch 69%Yara Moss — BBM 70%, SmartMatch 38%Nadia Vaughn — BBM 53%, SmartMatch 47%Ruben Bell — BBM 67%, SmartMatch 59%Grant Okonkwo — BBM 66%, SmartMatch 58%Lena Kowalski — BBM 65%, SmartMatch 59%Curtis Gallagher — BBM 55%, SmartMatch 44%Hassan Sayed — BBM 68%, SmartMatch 38%Yara Gallagher — BBM 44%, SmartMatch 53%Elena Raman — BBM 60%, SmartMatch 61%Devon Vaughn — BBM 48%, SmartMatch 6%Ruben Alvarez — BBM 68%, SmartMatch 61%Elena Haddad — BBM 69%, SmartMatch 63%Devon Moss — BBM 71%, SmartMatch 56%Elena Lindgren — BBM 53%, SmartMatch 40%Iris Ortiz — BBM 50%, SmartMatch 53%Simone Gallagher — BBM 67%, SmartMatch 56%Elena Vaughn — BBM 74%, SmartMatch 47%Iris Alvarez — BBM 50%, SmartMatch 52%Fiona Kowalski — BBM 66%, SmartMatch 58%Elena Reyes — BBM 44%, SmartMatch 48%Simone Moss — BBM 59%, SmartMatch 48%Hassan Foster — BBM 70%, SmartMatch 41%Hassan Haddad — BBM 73%, SmartMatch 41%Curtis Kowalski — BBM 75%, SmartMatch 73%Tomas Haddad — BBM 41%, SmartMatch 65%Lena Raman — BBM 58%, SmartMatch 69%Elena Ortiz — BBM 64%, SmartMatch 55%Fiona Lindgren — BBM 39%, SmartMatch 48%Curtis Raman — BBM 76%, SmartMatch 61%Priya Raman — BBM 69%, SmartMatch 55%Marcus Reyes — BBM 74%, SmartMatch 63%Owen Raman — BBM 43%, SmartMatch 5%Cora Haddad — BBM 48%, SmartMatch 65%Iris Moss — BBM 50%, SmartMatch 68%Simone Moss — BBM 62%, SmartMatch 64%Curtis Bell — BBM 50%, SmartMatch 59%Fiona Alvarez — BBM 49%, SmartMatch 46%Simone Reyes — BBM 65%, SmartMatch 59%Lena Gallagher — BBM 48%, SmartMatch 46%Cora Bryant — BBM 57%, SmartMatch 18%Lena Sayed — BBM 63%, SmartMatch 10%Ruben Gallagher — BBM 59%, SmartMatch 40%Dmitri Ortiz — BBM 42%, SmartMatch 57%Simone Bryant — BBM 71%, SmartMatch 60%Grant Okonkwo — BBM 75%, SmartMatch 60%Owen Alvarez — BBM 76%, SmartMatch 57%Elena Nakamura — BBM 61%, SmartMatch 45%Curtis Bryant — BBM 52%, SmartMatch 53%Elena Kowalski — BBM 76%, SmartMatch 54%Owen Raman — BBM 75%, SmartMatch 59%Andre Raman — BBM 78%, SmartMatch 44%Iris Raman — BBM 52%, SmartMatch 30%Owen Vaughn — BBM 44%, SmartMatch 49%Simone Webb — BBM 35%, SmartMatch 2%Dmitri Raman — BBM 57%, SmartMatch 4%Nadia Duval — BBM 49%, SmartMatch 61%Hassan Gallagher — BBM 57%, SmartMatch 19%Grant Lindgren — BBM 68%, SmartMatch 68%Cora Ortiz — BBM 55%, SmartMatch 78%Cora Moss — BBM 65%, SmartMatch 65%Elena Bryant — BBM 77%, SmartMatch 50%Tomas Alvarez — BBM 56%, SmartMatch 70%Ruben Hart — BBM 44%, SmartMatch 42%Simone Sayed — BBM 75%, SmartMatch 48%Wes Bryant — BBM 72%, SmartMatch 65%Iris Bell — BBM 73%, SmartMatch 56%Tomas Bryant — BBM 54%, SmartMatch 26%Yara Alvarez — BBM 53%, SmartMatch 6%Lena Reyes — BBM 31%, SmartMatch 32%Fiona Lindgren — BBM 51%, SmartMatch 63%Owen Ortiz — BBM 57%, SmartMatch 74%Tomas Alvarez — BBM 51%, SmartMatch 4%Marcus Haddad — BBM 61%, SmartMatch 63%Cora Webb — BBM 48%, SmartMatch 37%Marcus Foster — BBM 52%, SmartMatch 44%Priya Kowalski — BBM 32%, SmartMatch 13%Marcus Lindgren — BBM 56%, SmartMatch 63%Tomas Petrov — BBM 62%, SmartMatch 61%Tomas Ortiz — BBM 68%, SmartMatch 69%Grant Alvarez — BBM 42%, SmartMatch 35%Priya Reyes — BBM 76%, SmartMatch 69%Priya Gallagher — BBM 28%, SmartMatch 20%Lena Duval — BBM 48%, SmartMatch 51%
169 shown of 169 matched
FitBuyer Max offerHeadroom BBMSMDist
64Grant Alvarez$221,855$56,85570%79%1.6 mi
64Dmitri Reyes$221,502$56,50273%73%8.9 mi
64Priya Reyes$237,083$72,08376%69%4.2 mi
63Nadia Vaughn$220,135$55,13573%71%8.8 mi
63Curtis Kowalski$200,169$35,16975%73%5.1 mi
61Ruben Bell$234,863$69,86370%66%2.3 mi
61Curtis Raman$221,646$56,64676%61%6.1 mi
60Nadia Gallagher$212,019$47,01971%67%9.2 mi
60Grant Okonkwo$218,316$53,31675%60%5.1 mi
60Owen Raman$222,102$57,10275%59%11.5 mi
60Grant Lindgren$233,099$68,09968%68%15.6 mi
59Yara Okonkwo$216,333$51,33370%66%13.7 mi
59Owen Okonkwo$235,640$70,64075%54%2.4 mi
59Owen Nakamura$207,480$42,48068%69%11.1 mi
59Marcus Petrov$200,353$35,35370%68%14.5 mi
59Elena Haddad$235,411$70,41169%63%13.3 mi
59Marcus Reyes$200,276$35,27674%63%5.8 mi
59Owen Alvarez$225,149$60,14976%57%2 mi
59Wes Bryant$210,244$45,24472%65%6.1 mi
59Tomas Ortiz$207,794$42,79468%69%6.2 mi
58Wes Hart$220,478$55,47868%63%10.1 mi
58Dmitri Petrov$221,548$56,54877%53%7.6 mi
58Dmitri Kowalski$208,534$43,53471%62%9.9 mi
58Tomas Foster$213,296$48,29674%57%0.5 mi
58Owen Sayed$229,778$64,77863%69%15.7 mi
58Simone Bryant$222,382$57,38271%60%2.6 mi
58Elena Bryant$228,076$63,07677%50%1.1 mi
57Andre Raman$189,559$24,55963%74%4.4 mi
57Yara Petrov$201,872$36,87268%65%12.1 mi
57Grant Lindgren$216,124$51,12466%64%16.4 mi
57Wes Okonkwo$201,912$36,91266%67%5.1 mi
57Iris Bryant$229,141$64,14165%64%21 mi
57Tomas Duval$221,262$56,26257%76%15.1 mi
57Yara Hart$230,158$65,15862%66%18.2 mi
57Devon Moss$226,155$61,15571%56%9 mi
57Elena Kowalski$204,985$39,98576%54%5.8 mi
57Cora Moss$218,284$53,28465%65%17 mi
56Marcus Reyes$214,612$49,61262%66%7 mi
56Owen Bryant$201,368$36,36867%63%7.4 mi
56Owen Duval$213,923$48,92373%54%1.3 mi
56Hassan Ortiz$214,876$49,87667%60%18.1 mi
56Simone Haddad$232,400$67,40062%65%20.6 mi
56Hassan Lindgren$238,007$73,00774%46%7.9 mi
56Ruben Bell$229,985$64,98567%59%7 mi
56Simone Reyes$237,134$72,13465%59%16.2 mi
56Andre Raman$229,468$64,46878%44%4.8 mi
56Iris Bell$204,390$39,39073%56%6.3 mi
56Marcus Haddad$235,979$70,97961%63%19.9 mi
55Andre Reyes$222,691$57,69158%67%11.8 mi
55Priya Moss$215,139$50,13978%43%5.4 mi
55Hassan Hart$228,342$63,34264%58%14.3 mi
55Dmitri Webb$218,114$53,11463%62%14.4 mi
55Hassan Bryant$203,652$38,65260%69%21.9 mi
55Ruben Gallagher$205,473$40,47360%69%21.8 mi
55Priya Duval$207,285$42,28569%58%6.3 mi
55Grant Okonkwo$222,300$57,30066%58%15.9 mi
55Lena Kowalski$222,629$57,62965%59%6.1 mi
55Ruben Alvarez$193,946$28,94668%61%2.6 mi
55Simone Gallagher$225,319$60,31967%56%7.2 mi
55Elena Vaughn$230,127$65,12774%47%5.9 mi
55Fiona Kowalski$221,717$56,71766%58%15.6 mi
55Cora Ortiz$191,792$26,79255%78%8.2 mi
55Simone Sayed$217,461$52,46175%48%6.4 mi
55Owen Ortiz$205,277$40,27757%74%19.2 mi
54Grant Foster$215,979$50,97967%54%3.4 mi
54Cora Webb$201,816$36,81668%57%13 mi
54Dmitri Hart$218,425$53,42574%45%10.6 mi
54Tomas Hart$205,696$40,69662%63%3 mi
54Simone Bryant$234,974$69,97455%67%14.2 mi
54Elena Raman$222,995$57,99560%61%19.4 mi
54Lena Raman$207,615$42,61558%69%14.4 mi
54Priya Raman$198,910$33,91069%55%9.6 mi
54Simone Moss$200,178$35,17862%64%20 mi
54Tomas Petrov$215,840$50,84062%61%10.2 mi
53Owen Petrov$196,538$31,53866%56%19 mi
53Iris Bryant$204,686$39,68672%49%9.5 mi
53Tomas Reyes$222,804$57,80459%60%20.2 mi
53Elena Webb$225,743$60,74361%59%14.5 mi
53Dmitri Bryant$227,025$62,02568%48%15.4 mi
53Elena Ortiz$217,509$52,50964%55%15.8 mi
BuyerPulse + SmartMatch

Buyers don’t always buy what they say they buy.

A buy box tells you what somebody says they want. Their history tells you what they actually do.

Deal Room can look at both. It compares the property against the buyer’s stated criteria and against the deals their actual behavior says they are interested in.

That exposes buyers a simple buy-box search can miss — and buyers a buy-box search can make look better than they really are.

Match quality
0%0%25%25%50%50%75%75%100%100%SMARTMATCHBUY BOX MATCHTRUE FIT 96HIDDEN 18ASPIRATIONAL 53POOR FIT 33Fiona Alvarez — BBM 40%, SmartMatch 35%Grant Petrov — BBM 54%, SmartMatch 45%Andre Raman — BBM 63%, SmartMatch 74%Grant Kowalski — BBM 46%, SmartMatch 59%Tomas Okonkwo — BBM 42%, SmartMatch 4%Hassan Bryant — BBM 53%, SmartMatch 16%Simone Foster — BBM 52%, SmartMatch 64%Curtis Vaughn — BBM 47%, SmartMatch 46%Simone Lindgren — BBM 49%, SmartMatch 55%Andre Reyes — BBM 58%, SmartMatch 67%Grant Foster — BBM 67%, SmartMatch 54%Wes Hart — BBM 68%, SmartMatch 63%Nadia Vaughn — BBM 73%, SmartMatch 71%Wes Haddad — BBM 64%, SmartMatch 48%Cora Webb — BBM 68%, SmartMatch 57%Dmitri Petrov — BBM 50%, SmartMatch 11%Dmitri Hart — BBM 74%, SmartMatch 45%Curtis Foster — BBM 51%, SmartMatch 52%Yara Petrov — BBM 68%, SmartMatch 65%Marcus Reyes — BBM 62%, SmartMatch 66%Grant Webb — BBM 47%, SmartMatch 45%Andre Raman — BBM 59%, SmartMatch 61%Priya Bryant — BBM 59%, SmartMatch 47%Tomas Vaughn — BBM 53%, SmartMatch 46%Simone Webb — BBM 72%, SmartMatch 46%Simone Hart — BBM 47%, SmartMatch 69%Wes Hart — BBM 55%, SmartMatch 50%Curtis Gallagher — BBM 35%, SmartMatch 8%Ruben Vaughn — BBM 58%, SmartMatch 57%Yara Okonkwo — BBM 70%, SmartMatch 66%Dmitri Okonkwo — BBM 54%, SmartMatch 12%Owen Petrov — BBM 66%, SmartMatch 56%Dmitri Bryant — BBM 40%, SmartMatch 16%Priya Moss — BBM 78%, SmartMatch 43%Owen Bryant — BBM 67%, SmartMatch 63%Grant Lindgren — BBM 66%, SmartMatch 64%Owen Okonkwo — BBM 75%, SmartMatch 54%Owen Haddad — BBM 33%, SmartMatch 9%Andre Petrov — BBM 52%, SmartMatch 55%Wes Okonkwo — BBM 66%, SmartMatch 67%Hassan Hart — BBM 64%, SmartMatch 58%Ruben Bell — BBM 70%, SmartMatch 66%Yara Moss — BBM 48%, SmartMatch 5%Iris Hart — BBM 42%, SmartMatch 40%Dmitri Webb — BBM 63%, SmartMatch 62%Hassan Gallagher — BBM 39%, SmartMatch 10%Grant Lindgren — BBM 54%, SmartMatch 49%Dmitri Vaughn — BBM 40%, SmartMatch 60%Cora Okonkwo — BBM 62%, SmartMatch 59%Nadia Webb — BBM 53%, SmartMatch 3%Lena Gallagher — BBM 50%, SmartMatch 65%Owen Nakamura — BBM 68%, SmartMatch 69%Curtis Bryant — BBM 50%, SmartMatch 55%Iris Bryant — BBM 65%, SmartMatch 64%Hassan Bryant — BBM 60%, SmartMatch 69%Dmitri Kowalski — BBM 62%, SmartMatch 50%Owen Gallagher — BBM 43%, SmartMatch 72%Owen Duval — BBM 73%, SmartMatch 54%Yara Sayed — BBM 63%, SmartMatch 16%Cora Kowalski — BBM 60%, SmartMatch 38%Nadia Ortiz — BBM 56%, SmartMatch 10%Simone Hart — BBM 59%, SmartMatch 45%Yara Bell — BBM 43%, SmartMatch 1%Dmitri Petrov — BBM 77%, SmartMatch 53%Simone Foster — BBM 51%, SmartMatch 65%Curtis Petrov — BBM 62%, SmartMatch 18%Nadia Gallagher — BBM 71%, SmartMatch 67%Devon Hart — BBM 55%, SmartMatch 37%Wes Kowalski — BBM 54%, SmartMatch 21%Marcus Vaughn — BBM 62%, SmartMatch 48%Dmitri Kowalski — BBM 71%, SmartMatch 62%Owen Duval — BBM 57%, SmartMatch 60%Tomas Foster — BBM 74%, SmartMatch 57%Tomas Duval — BBM 57%, SmartMatch 76%Andre Haddad — BBM 57%, SmartMatch 52%Curtis Haddad — BBM 48%, SmartMatch 8%Iris Bryant — BBM 72%, SmartMatch 49%Iris Sayed — BBM 45%, SmartMatch 65%Andre Vaughn — BBM 43%, SmartMatch 54%Grant Duval — BBM 41%, SmartMatch 2%Yara Vaughn — BBM 45%, SmartMatch 57%Yara Hart — BBM 62%, SmartMatch 66%Owen Sayed — BBM 63%, SmartMatch 69%Curtis Haddad — BBM 57%, SmartMatch 4%Hassan Ortiz — BBM 67%, SmartMatch 60%Tomas Reyes — BBM 59%, SmartMatch 60%Ruben Foster — BBM 23%, SmartMatch 32%Tomas Hart — BBM 62%, SmartMatch 63%Andre Haddad — BBM 30%, SmartMatch 6%Simone Bryant — BBM 55%, SmartMatch 67%Simone Haddad — BBM 62%, SmartMatch 65%Hassan Lindgren — BBM 74%, SmartMatch 46%Cora Ortiz — BBM 25%, SmartMatch 3%Marcus Petrov — BBM 70%, SmartMatch 68%Elena Duval — BBM 54%, SmartMatch 58%Hassan Gallagher — BBM 52%, SmartMatch 49%Grant Alvarez — BBM 70%, SmartMatch 79%Owen Vaughn — BBM 50%, SmartMatch 18%Nadia Moss — BBM 41%, SmartMatch 27%Wes Moss — BBM 51%, SmartMatch 3%Hassan Alvarez — BBM 59%, SmartMatch 8%Dmitri Reyes — BBM 73%, SmartMatch 73%Ruben Gallagher — BBM 60%, SmartMatch 69%Priya Duval — BBM 69%, SmartMatch 58%Ruben Foster — BBM 62%, SmartMatch 28%Hassan Reyes — BBM 40%, SmartMatch 52%Iris Bryant — BBM 38%, SmartMatch 52%Simone Hart — BBM 55%, SmartMatch 44%Yara Raman — BBM 52%, SmartMatch 60%Ruben Nakamura — BBM 48%, SmartMatch 66%Wes Foster — BBM 36%, SmartMatch 8%Fiona Kowalski — BBM 48%, SmartMatch 60%Priya Gallagher — BBM 64%, SmartMatch 2%Elena Webb — BBM 61%, SmartMatch 59%Elena Kowalski — BBM 49%, SmartMatch 34%Dmitri Bryant — BBM 68%, SmartMatch 48%Andre Gallagher — BBM 52%, SmartMatch 69%Yara Moss — BBM 70%, SmartMatch 38%Nadia Vaughn — BBM 53%, SmartMatch 47%Ruben Bell — BBM 67%, SmartMatch 59%Grant Okonkwo — BBM 66%, SmartMatch 58%Lena Kowalski — BBM 65%, SmartMatch 59%Curtis Gallagher — BBM 55%, SmartMatch 44%Hassan Sayed — BBM 68%, SmartMatch 38%Yara Gallagher — BBM 44%, SmartMatch 53%Elena Raman — BBM 60%, SmartMatch 61%Devon Vaughn — BBM 48%, SmartMatch 6%Ruben Alvarez — BBM 68%, SmartMatch 61%Elena Haddad — BBM 69%, SmartMatch 63%Devon Moss — BBM 71%, SmartMatch 56%Elena Lindgren — BBM 53%, SmartMatch 40%Iris Ortiz — BBM 50%, SmartMatch 53%Simone Gallagher — BBM 67%, SmartMatch 56%Elena Vaughn — BBM 74%, SmartMatch 47%Iris Alvarez — BBM 50%, SmartMatch 52%Fiona Kowalski — BBM 66%, SmartMatch 58%Elena Reyes — BBM 44%, SmartMatch 48%Simone Moss — BBM 59%, SmartMatch 48%Hassan Foster — BBM 70%, SmartMatch 41%Hassan Haddad — BBM 73%, SmartMatch 41%Curtis Kowalski — BBM 75%, SmartMatch 73%Tomas Haddad — BBM 41%, SmartMatch 65%Lena Raman — BBM 58%, SmartMatch 69%Elena Ortiz — BBM 64%, SmartMatch 55%Fiona Lindgren — BBM 39%, SmartMatch 48%Curtis Raman — BBM 76%, SmartMatch 61%Priya Raman — BBM 69%, SmartMatch 55%Marcus Reyes — BBM 74%, SmartMatch 63%Owen Raman — BBM 43%, SmartMatch 5%Cora Haddad — BBM 48%, SmartMatch 65%Iris Moss — BBM 50%, SmartMatch 68%Simone Moss — BBM 62%, SmartMatch 64%Curtis Bell — BBM 50%, SmartMatch 59%Fiona Alvarez — BBM 49%, SmartMatch 46%Simone Reyes — BBM 65%, SmartMatch 59%Lena Gallagher — BBM 48%, SmartMatch 46%Cora Bryant — BBM 57%, SmartMatch 18%Lena Sayed — BBM 63%, SmartMatch 10%Ruben Gallagher — BBM 59%, SmartMatch 40%Dmitri Ortiz — BBM 42%, SmartMatch 57%Simone Bryant — BBM 71%, SmartMatch 60%Grant Okonkwo — BBM 75%, SmartMatch 60%Owen Alvarez — BBM 76%, SmartMatch 57%Elena Nakamura — BBM 61%, SmartMatch 45%Curtis Bryant — BBM 52%, SmartMatch 53%Elena Kowalski — BBM 76%, SmartMatch 54%Owen Raman — BBM 75%, SmartMatch 59%Andre Raman — BBM 78%, SmartMatch 44%Iris Raman — BBM 52%, SmartMatch 30%Owen Vaughn — BBM 44%, SmartMatch 49%Simone Webb — BBM 35%, SmartMatch 2%Dmitri Raman — BBM 57%, SmartMatch 4%Nadia Duval — BBM 49%, SmartMatch 61%Hassan Gallagher — BBM 57%, SmartMatch 19%Grant Lindgren — BBM 68%, SmartMatch 68%Cora Ortiz — BBM 55%, SmartMatch 78%Cora Moss — BBM 65%, SmartMatch 65%Elena Bryant — BBM 77%, SmartMatch 50%Tomas Alvarez — BBM 56%, SmartMatch 70%Ruben Hart — BBM 44%, SmartMatch 42%Simone Sayed — BBM 75%, SmartMatch 48%Wes Bryant — BBM 72%, SmartMatch 65%Iris Bell — BBM 73%, SmartMatch 56%Tomas Bryant — BBM 54%, SmartMatch 26%Yara Alvarez — BBM 53%, SmartMatch 6%Lena Reyes — BBM 31%, SmartMatch 32%Fiona Lindgren — BBM 51%, SmartMatch 63%Owen Ortiz — BBM 57%, SmartMatch 74%Tomas Alvarez — BBM 51%, SmartMatch 4%Marcus Haddad — BBM 61%, SmartMatch 63%Cora Webb — BBM 48%, SmartMatch 37%Marcus Foster — BBM 52%, SmartMatch 44%Priya Kowalski — BBM 32%, SmartMatch 13%Marcus Lindgren — BBM 56%, SmartMatch 63%Tomas Petrov — BBM 62%, SmartMatch 61%Tomas Ortiz — BBM 68%, SmartMatch 69%Grant Alvarez — BBM 42%, SmartMatch 35%Priya Reyes — BBM 76%, SmartMatch 69%Priya Gallagher — BBM 28%, SmartMatch 20%Lena Duval — BBM 48%, SmartMatch 51%
  • True fit — Their criteria say yes. Their behavior says yes. Call them first.
  • Hidden gem — Their stated criteria say this deal should not be a great match. Their behavior says they actually buy deals like it. Send it anyway.
  • Aspirational — Their buy box looks perfect. Their actual behavior does not. Don’t count them as heavily as they look on paper.
  • Poor fit — Neither the criteria nor the behavior supports the deal. Move on.

Don’t just find the buyers whose settings match. Find the buyers whose behavior does.

Now change the deal

The repair estimate just went up $12,000. What happens now?

The buyer now has another $12,000 invested.

Their return falls. Some buyers may no longer fit. The seller offer may need to come down. There may be less room for your assignment fee. OfferAid may prefer a different structure. MaxFee may identify a different profit range.

Now imagine the ARV changes. Or the seller refuses your number. Or you decide you want another $10,000 on the assignment.

Deal Room recalculates the consequences around the new deal.

Because changing one part of a real deal changes the rest of it.

Go deeper when the deal calls for it

The rest of the analysis, for when a deal turns on something else.

Most deals are decided by the value, the repairs, the offer, your fee and the buyers. When a particular deal turns on something else, the analysis is already there.

Pressure-test the exit

Compare your ARV against independent valuation data and look at the less comfortable versions of the deal.

  • Break-even resale — the price where the buyer stops making money.
  • Downside cushion — how far value can fall before it reaches that point.
  • ARV premium — how much of the deal depends on defending a number above the range.
  • Multiple exits — the buyer’s profit and ROI at different resale values.
If the buyer keeps it

Rent estimates, gross yield, NOI, cap-rate views, cash-on-cash, gross rent multiplier and break-even rent — for when the likely exit is a rental rather than a flip.

If you change the property

Model an addition before committing to it: build cost, extra holding time, the new ARV and buyer return, and the value created per construction dollar.

It also accounts for what simple renovation math ignores — the next square foot is not necessarily worth what the average existing square foot is worth, and the neighborhood eventually stops paying you for making the house bigger.

The property record

Beds, baths, square footage, lot, ownership, taxes, debt and equity information and distress indicators, alongside the sold comps you choose to support your ARV.

The seller stays with the deal

Conversation, motivation, timeline, condition, occupancy, asking price and notes — beside the numbers, because the best offer means nothing if it ignores the person accepting it.

Then act on it

Generate the offer letter, send the contract, save the analysis, push the deal to the repository, launch the full estimator or lock the deal when the numbers are ready.

Humphrey

Ask the deal what you’re missing.

There is a lot behind one offer. The seller conversation. The property. The comps. The ARV. The repair scope. The offer. Your fee. The buyers. The activity and notes that got you here.

Humphrey can reason across that deal context with you.

  • What did the seller actually say they want?
  • Why is the repair number what it is?
  • What is the strongest objection I should expect?
  • Where is this deal weak?
  • What am I still missing before I make an offer?
  • What happens if I change my fee?

And it knows the difference between the facts of the deal and the numbers you still control.

Ask Humphrey to make a deal more profitable and it will move your offer, your fee and the strategy. It will not quietly raise your ARV or cut the repair number to get there — those come from the comps and the scope, and inflating them does not improve the deal, it just makes the sheet wrong in front of a buyer running their own numbers.

When it proposes a change to the live numbers, the change is shown before it is applied. You approve or decline it, and applied changes can be undone.

H
Humphreyreading the seller conversation, notes, activity, the linked scope and the live deal
what did the seller actually say they want?
They inherited the house and it has been vacant since. Their words were that they just want it handled — that is a speed motivation, not a price one. They agreed to a Thursday afternoon walkthrough. Nothing in the thread mentions a number yet.
make this deal as profitable as possible
Two of these numbers aren’t mine to move. ARV and repairs are facts — they come from the comps and a committed scope. What I can move is what you choose. Here is the most aggressive structure that still clears the buyers in scope.
Approve this change?Assignment fee $34,000 → $25,000 DeclineApprove
Straight answers

The questions people actually ask.

What do I need to start analyzing a property?

An address is enough to begin evaluating a property. Seller information can be added when you want to turn it into a lead and keep the seller relationship connected to the analysis.

Where do the property numbers come from?

Deal Room brings together the available property record, comparable sales, valuation information and the numbers you enter or choose for the deal. The interface also shows the source of important values so you can decide what you trust.

Do I have to use Deal Room’s recommended numbers?

No. You control the ARV, seller offer, repair number and assignment fee. Features such as OfferAid and MaxFee are there to show the consequences and tradeoffs of those choices.

Can Deal Room use my repair estimate?

Deal Room can link an EstiMate scope to the property and use that scope as the repair number driving the analysis. Finish tiers can be changed, and updated scopes can be surfaced without silently overwriting the number already being used.

How does Buyer Match decide who fits the deal?

The buyer view can evaluate both stated buying criteria and actual purchasing behavior, along with the economics of the current deal. The result is meant to show not merely who is on a list, but who appears capable of buying this specific property at this specific structure.

Can Humphrey change my deal without me approving it?

No. Proposed live changes are shown before they are applied. You can approve or decline them, and applied changes can be undone. Saving or locking the deal remains a separate user action.

Your next deal

Before you put it under contract, know who the deal has to work for.

Know what the seller can get.
Know what you can make.
Know what the buyer can pay.
Know whether the deal survives when the assumptions change.

Then make the offer.