ALEX · Executive Summary

103 Maria Court, Georgetown

Comparable-sales valuation · Listed Property Buyer Model · Williamson County

Prepared for Gary C. BlackburnPrepared by Diane Hart Alexander, MBA, MHA, Designated BrokerSummary revised October 10, 2026, 9:48 PM CDTReport version 14.294

What the two Models share. Both models start from the same sales. But at that point they dramatically differ.

ALEX AI Statistical Model

ALEX’s statistical model uses five comparable sales. Each sale’s adjusted price is carried to this home’s 2,104 SF using the size effect measured from these same sales. Each sale is then weighted by how closely it matches this home on size, age, lot, rooms, distance and date of sale. The average and median are weighted; the minimum and maximum are the lowest and highest single sale.

Traditional Arithmetic Model

The same five sales, with no adjustments: the average sale price per SF times this home’s 2,104 SF, every sale counted equally. Section 03 explains how the two Models differ.

Arithmetic Model. The math is simple:

1. Sale Price per SF for Each Home. For each home used as a comparable sale, divide its sale price by its total SF.
2. Average Sale Price per SF. Add the Sale Price per SF for Each Home, then divide by the number of homes.
3. Your Home’s SF. Use the SF on the MLS listing, the same source as every comparable sale.
4. Your Home’s Estimated Value. Average Sale Price per SF multiplied by Your Home’s SF.

Example. $200 (Average Sale Price per SF) × 2,500 SF (Your Home’s SF) = $500,000 (Your Home’s Estimated Value).

ALEX Model. It is completely logical, statistically accurate and defensible at an MIT PhD Dissertation Committee Review. But it requires an NVIDIA mini, or something equivalent, to do the math. The detailed report sets out the full method.

Compared on the average alone, ALEX’s valuation is lower than the traditional arithmetic figure. The difference is shown below in dollars and as a percentage of the traditional average.

The suggested offer spans the ALEX AI Statistical Model’s average and median. While the list-price box is ticked it is held at the asking price.

All four are net: what the house costs after the $8,000 credit, the contract price less the credit. How they are set. We looked at 392 similar homes that sold in Georgetown, $400k to $600k. About 1 in 6 sold further below their asking price than the lowest offer. About 1 in 6 sold above the highest. The valuation does not set the range. The highest is held at the asking price, the default. The evidence alone would reach $412,000 net.

Of similar homes that sold:

• 12%–18% went for the lowest offer or less.
• 97% went for the net adjusted value or less.
• 75%–82% went for the highest offer or less.

This is not the chance the seller accepts. The MLS records sales, not refused offers.

Move the price, not the credit. Because you are financing 100% with no down payment, the seller-paid closing costs help you close the VA purchase. Giving them up raises the cash you must bring to closing. If you need to offer more, raise the contract price and keep the full credit in place.

Every counter in the table below keeps the full $8,000 credit, and we recommend that any counter you make does the same. Each row shows three things. First, what the house effectively costs you: the contract price less the credit. Second, the cash you bring to closing. Third, the monthly payment on the loan terms above.

Where it stands. Your offer: the full asking price, with the credit.

Split the difference. Your effective price halfway between the offer’s and the model average, rounded down to the nearest $100.

Ceiling. Held at the asking price, the default; the counter at the valuation would be above list.

Each counter raises the contract price further above the valuation. If the Notice of Value comes in at the valuation, the difference is paid in cash or the contract can be terminated.

All five are in Sierra Vista, this house’s own subdivision. In each table the first column is this house and each column after it is one comparable sale. Dollar amounts are rounded to the nearest dollar, and each total is the sum of the lines shown above it.

Scaling to 2,104 SF. Every home that sold is smaller than this one. To compare them fairly, each sale’s price is first restated at this home’s size, using the sale’s own price per SF. For example, 312 Susana Dr has 1,960 SF. Its price after the time adjustment and all feature adjustments, including any made by Diane, is $419,446. Restated at this home’s size: $419,446 × 2,104 ÷ 1,960 = $450,262. The scaling line for 312 Susana Dr is the difference, +$30,816.

Statistical size adjustment. Scaling at a sale’s own price per SF assumes that price per SF stays the same as size changes. It does not. Smaller homes usually sell for more per SF than larger ones. Every home has a kitchen, bathrooms and a lot, and those cost about the same whatever its size. Scaling alone therefore overstates the value of a sale smaller than this home and understates the value of a larger one. ALEX measured, from these five sales, how price per SF changes with size, and applies that correction to each sale. For 312 Susana Dr the statistical size adjustment is −$13,758, so its total size adjustment is +$17,058. The further a sale’s size is from this home’s, the larger the correction.