prepared by bob mckenna licensed property tax consultant · 2020-04-16 · bedroom count 3.0 3.0 $0...

2510 ROSEWOOD AVE, AUSTIN 1

Appraisal Contestation Report for 2510 Rosewood Ave, Austin

Prepared by Bob McKenna

Licensed Property Tax Consultant


Appraisal Contestation Report for 2510 Rosewood Ave, Austin

Introduction

This report presents detailed evidence to contest the appraisal of the subject property, 2510

Rosewood Ave. The appraisal is contested because: 1. The Appraised Value is unequal compared with

other properties; and 2. The Appraised Value is not calculated at the property’s market value.

To be appraised in an equal and uniform manner, the appraisal district should set the appraised value

of the subject property to $643,969. To match the market value of the subject property as of January 1,

2020, the appraisal district should set the appraised value of the property to $608,800. Texas courts have

held that “If a conflict exists between taxation at market value and equal and uniform taxation, equal and

uniform taxation must prevail.” Consequently, the appraisal district should appraise the subject property at

$643,969 unless the equal and uniform contestation is rejected. If it is rejected, then the appraisal district

should instead appraise the subject property at $608,800.

Data utilized in this report is sourced from a combination of Travis County public data, publicly

available databases collected and distributed by Zillow and its affiliates, and personal attestation.


About the Subject Property

2510 Rosewood Ave is a single-family home located in the 78702 ZIP Code of Austin. It has 3

bedrooms and 2.5 bathrooms. It is a 2,441 square foot home on a lot of 8,187 square feet. The home was

built in 2012.

The maps on the following page show the location of 2510 Rosewood Ave with a red pin. The first

map situates the property within Travis County. The second map zooms in on its immediate neighborhood

in Austin.


Figure 1. 2510 Rosewood Ave within Austin and Travis County.

Figure 2. The immediate neighborhood of 2510 Rosewood Ave.


2510 Rosewood Ave is Not Appraised in an Equal and Uniform Manner

Summary of Results

The subject property, 2510 Rosewood Ave, is not appraised in an equal and uniform manner because

the appraised value of the property ($669,865) is greater than the median appraised value of a set of 5

comparable properties when appropriately adjusted ($643,969). Consequently, the appraisal district must

adjust their appraised value of the subject property to $643,969.

Comparable Properties and Adjustments

Following the guidelines found in The Appraisal of Real Estate, we assess property similarity

between our subject and its comparables on the basis of absolute adjustment in appraisal price, rather than

simply the net change in price from the adjustments.

Comparable Number Comparable Address Adjusted Appraisal

1 1412 Harvey St $670,130

2 2101 S L Davis Ave $683,054

3 2601 Sol Wilson Ave Unit#b $638,812

4 2601 Sol Wilson Ave Unit#a $638,873

5 1813 Maple Ave $643,969

Median Adjusted Appraisal $643,969


Comparable Property 1

Adjustment Grid

All adjustments are performed on a per-square-foot (PSF) basis. The Building SF adjustment reflects

that smaller properties are typically more valuable on a PSF basis than larger properties.

Attribute Subject Property Comparable 1 Adjustment (PSF)

Address 2510 Rosewood Ave 1412 Harvey St

ZIP Code 78702 78702 $0

Property Type A1 A1 $0

Bedroom Count 3.0 4.0 $0

Bathroom Count 2.5 3.5 $0

Year Built 2012 2013 $0

Building SF 2,441 2,452 $0

Lot SF 8,187 12,002 $-6

Neighborhood Code E3009 E3009 $0

Quality Code 5 5 $0

Appraisals and Adjustments

Comparable 1 Appraised Value PSF: $280

Comparable 1 Net Adjustments PSF: $-5

Comparable 1 Adjusted Value PSF: $275

By using the Subject Property’s value of 2,441 Square Feet, we arrive at the Comparable 1

Adjusted Appraised Value: $670,130

Property Similarity

Distance from Comparable 1 to Subject Property: 0.47 miles

Absolute Adjustments as Percent of Appraisal: 2.2%



Adjustment Grid




Address 2510 Rosewood Ave 2101 S L Davis Ave

ZIP Code 78702 78702 $0




Year Built 2012 2014 $0

Building SF 2,441 2,434 $0

Lot SF 8,187 7,962 $0

Neighborhood Code E3009 E2010 $-3

Quality Code 5 5 $0







Property Similarity





Adjustment Grid




Address 2510 Rosewood Ave 2601 Sol Wilson Ave Unit#b

ZIP Code 78702 78702 $0




Year Built 2012 2014 $0

Building SF 2,441 2,301 $-4

Lot SF 8,187 4,068 $6

Neighborhood Code E3009 E311CP $0

Quality Code 5 5 $0



Comparable 3 Net Adjustments PSF: $2




Property Similarity





Adjustment Grid




Address 2510 Rosewood Ave 2601 Sol Wilson Ave Unit#a

ZIP Code 78702 78702 $0




Year Built 2012 2014 $0

Building SF 2,441 2,301 $-4

Lot SF 8,187 4,068 $6

Neighborhood Code E3009 E311CP $0

Quality Code 5 5 $0







Property Similarity





Adjustment Grid




Address 2510 Rosewood Ave 1813 Maple Ave

ZIP Code 78702 78702 $0




Year Built 2012 2007 $0

Building SF 2,441 2,497 $2

Lot SF 8,187 5,938 $3

Neighborhood Code E3009 E2010 $-3

Quality Code 5 5 $0







Property Similarity




Equal and Uniform Calculation Methodology

Legal Background. Texas Property Tax Code Section 42.26 mandates that property owners are

entitled to relief if they can demonstrate that the appraised value of their property “exceeds the median

appraised value of a reasonable number of comparable properties appropriately adjusted”.

The State Comptroller’s publication “Property Tax Basics” adds additional administrative

interpretation to this statute. That publication suggests a three-pronged test for deciding if a property

owner’s protest on an equal and uniform basis should succeed:

1. The number of comparable properties selected is reasonable. (The document suggests that three to

ten properties is considered reasonable).

2. That each of the properties is comparable to the subject property, and

3. That the value of those comparable properties has been appropriately adjusted, following generally

accepted standards.

Adjustment Model. Our adjustment model is specifically tuned to the subject property and its

comparables, is based on accurate and relevant data, and follows generally accepted practices in its

development and use.

Table 18.1 from the 14th (most recent) edition of The Appraisal of Real Estate, one of the books that

Texas law prescribes as generally accepted, suggests that appraisal adjustments for single-unit residential

properties are typically done either on a price or on a price-per-square-foot basis. Following these

guidelines we implemented an adjustment model on a price-per-square-foot basis. We provide additional

detail on our modeling process to demonstrate that our adjustment methodology follows generally accepted

guidelines.

As suggested by The Appraisal of Real Estate, we created a multiple linear regression adjustment

model that takes into account the ZIP code, property type, number of bedrooms and bathrooms, the square

footage of the building and lot, and the neighborhood code and a numerical encoding for the quality code

that has been assigned by the appraisal district. We also have accurate bedroom data for a large number of

homes by using data from recent home sales.

As input to train our adjustment model, we considered all appraised properties located within 5

miles of the subject property. We then filtered down the set of appraised properties to eliminate outliers that


could otherwise skew our results. The appraised properties we considered must satisfy all of the following

conditions:

• At least 100 square feet.

• Not owned by a municipality, the county, or a government entity like a housing authority.

• Have valid quality and neighborhood codes for appraisal.

• Have at least one bathroom.

• Have no more than three bathrooms more than the subject property.

• Have no more than three bedrooms more than the subject property, for properties with a known

number of bedrooms.

• Be built no more than 30 years before or after the subject property.

• Have no more than two times the square footage of the subject property.

• Have no more than two times the lot size of the subject property.

• Have a neighborhood code that is common to at least 10 properties within the filtered set.

We end up with a set of 20,124 appraised properties that meet these conditions. Since many of the

qualities are collinear (for instance, number of bedrooms, bathrooms, and square footage are all correlated)

we solve for a model that has a regularization parameter to prevent overfitting and is restricted to positive

coefficients in the dimensions we expect to be positive. We use five-fold cross-validation to validate our

regression choices and modeling; the model we produce is the most accurate averaged over the holdout

sets. Five-fold cross-validation means there is a 20% holdout set on each fold, precisely matching the

guidance given in The Appraisal of Real Estate.


2510 Rosewood Ave is Not Appraised at its Market Value

Summary of Results

The subject property, 2510 Rosewood Ave, is not appraised at its market value because the appraised

value of the property ($669,865) is inconsistent with the property’s market value on January 1, 2020 when

calculated using two generally accepted appraisal methods and techniques: Adjusted Comparable Sales

($616,739) and an Automated Valuation Model ($600,862).

These two values create a so-called “bracket" for the subject property’s market value on January 1,

2020. To match the property’s market value, the appraisal district should set the appraised value of the

property to the arithmetic mean of the two methods: $608,800.

The evidence and methodologies for our Adjusted Comparable Sales value and Automated

Valuation Model value are outlined in the following pages.


Adjusted Comparable Sales

To create a sales comparison model, we begin by training an adjustment model specifically tuned to

the subject property and its potential comparables based on actual sales data. We use that adjustment model

to adjust the sale price of each potential comparable sale to match the attributes of the subject property.

How much we need to adjust the sale price is generally the largest factor in determining how similar the

subject property is to a potential comparable sale.

We then selected 3 comparable sales based on their similarities to the subject property. We adjust

those comparable sales based on our model, and then produce an estimated sale price for the property on

January 1, 2020 by doing a similarity-weighted average of the adjusted comparable sales.

Comparable Number Similarity to Subject Property Adjusted Sale Price

1 0.76 $645,042

2 0.75 $593,477

3 0.75 $611,090

Similarity-Weighted Estimated Sale Price $616,739

NB: For legal reasons related to data sharing, we have obscured certain identifying

information for the comparable sales listed on the following pages.





Adjustment Grid


Address 2510 Rosewood Ave 10•• Muse Ln ••••••••

ZIP Code 78702 78702 $0

Property Type A1 A4 $-2



Year Built 2012 2016 $-4

Building SF 2,441 1,7•• None

Lot SF 8,187 2,7•• $17

Sale Date – October 29, 2019 $2

Sale Price Adjustments

Comparable 1 Sale Price PSF: $279


Comparable 1 Adjusted Sale Price PSF: $264


Adjusted Sale Price: $645,042

Property and Sale Similarity


Absolute Adjustments as Percent of Sale Price: 18.8%

Sale Date in Decimal Years Before January 1, 2020: 0.18

Sale Similarity Measure: 0.76





Adjustment Grid


Address 2510 Rosewood Ave 27•• Halcyon D•••••••••

ZIP Code 78702 78702 $0




Year Built 2012 2015 $-3


Lot SF 8,187 3,0•• $16

Sale Date – August 16, 2019 $5
















Adjustment Grid


Address 2510 Rosewood Ave 11•• Euphoria Bn••••••••••

ZIP Code 78702 78702 $0




Year Built 2012 2015 $-3


Lot SF 8,187 2,6•• $17

Sale Date – November 21, 2019 $1













Sales Comparison and Adjustment Methodology

Legal Background. Texas Property Tax Code Section 42.23 mandates that property in Texas, with

limited exceptions, should be appraised at its market value as of January 1, and that the market value

should be determined based on generally accepted appraisal methods and techniques.

The Travis County Appraisal District writes that “In the absence of a sale of the subject, sales prices

of comparable properties are usually considered the best evidence of market value.” Adjusted Comparable

Sales is a generally accepted appraisal method that incorporates sale prices.

Comparable Sales Adjustment Model. To ensure adherence to generally accepted standards, we

referenced the 14th (most recent) edition of The Appraisal of Real Estate in the development of our

adjustment model. Furthermore, our adjustment model is specifically tuned to the subject property and its

comparables. The data used in the model is accurate and relevant data, as described in the Introduction of

this report.

Table 18.1 from the 14th (most recent) edition of The Appraisal of Real Estate, one of the books that

Texas law prescribes as generally accepted, suggests that appraisal adjustments for single-unit residential

properties are typically done either on a price or on a price-per-square-foot basis. Following these

guidelines we implemented an adjustment model on a price-per-square-foot basis. We provide additional

detail on our modeling process to demonstrate that our adjustment methodology follows generally accepted

guidelines.

As suggested by The Appraisal of Real Estate and required by Texas law, we created a multiple

linear regression adjustment model that takes into account the ZIP code, property type, number of

bedrooms and bathrooms, the square footage of the building and lot, and the date of sale. As required by

Texas law, we only consider sales within the 36 months prior to January 1, 2020.

As input to train our adjustment model, we considered all sold properties located within 10 miles of

the subject property. We then filtered down the set of sold properties to eliminate outliers that could

otherwise skew our results. The sold properties we considered must satisfy all of the following conditions:

• At least 100 square feet.

• Have at least 1 bathroom.

• Have no more than three bathrooms more than the subject property.


• Have no more than three bedrooms more than the subject property.

• Be built no more than 30 years before or after the subject property.

• Have no more than two times the square footage of the subject property.

• Have no more than two times the lot size of the subject property.

We end up with a set of 9,629 property sales that meet these conditions.

Measuring and Weighting Similarity. Following the suggestions of The Appraisal of Real Estate

we assess property similarity between our subject property and its comparables using three inputs:

1. The absolute adjustment in sale price as a fraction of the sale price.

2. The date of the sale.

3. The distance from the subject property.

Consider a comparable sale i. Let the absolute adjustment in sale price as a fraction of the sale price

be ai ≥ 0, let the date of the sale, expressed as a decimal number of years before January 1, 2020 be

di ≥ 0, and let the distance between the comparable and the subject property be ∆i ≥ 0. Our similarity

function then sets the similarity of comparable i to the subject property, si, by:

si = 1 − ai − 0.1d2i − 0.1∆i

We believe this is a reasonable choice of function. It treats a (very small) 2.5% absolute sale

adjustment identically to six months of time passing or a quarter-mile of distance. It treats a (modest) 10%

absolute sale adjustment identically to one year of time passing or one mile of distance. Furthermore, the

similarity function has the mathematical characteristic that (only) the sale of the subject property on

January 1, 2020 would have a similarity of 1.0.

Finally, following the guidelines found in The Appraisal of Real Estate, we combine different

adjusted sale prices not by using a mean or median but rather by doing an adjustment that takes into

account the relative similarity of each comparable sale to our subject property. We produce the estimated

sale price e by weighting each adjusted comparable sale price pi by its relative similarity si:

e =∑

i pi × si∑i si


Estimated Sale Price over Time. Texas Property Tax Code Section 23.13(c) is explicit about the

need to adjust any comparable sale for the date of the sale. Because our adjustment model uses multiple

linear regression with the date of sale as a direct input, we can easily demonstrate how our estimated sale

price of 2510 Rosewood Ave has changed over time.

Figure 3. The estimated sale price of 2510 Rosewood Ave over the past three years.

Figure 3 shows our estimate of the sale price of 2510 Rosewood Ave over the past three years. This

time effect has been applied to all of the comparable sales used to develop our price estimate.


Appraisal by an Automated Valuation Model

Modern Automated Valuation Models (AVMs) use machine learning algorithms to estimate

property values. These models figure out the combined property value of various features associated with a

home, including standard ones like square footage and number of bedrooms and more interesting ones like

where the home is (i.e., neighborhood, school zoning, etc.). Using optimization techniques drawn from

artificial intelligence, the valuation model figures out how best to combine these features in order to create

the most accurate predictive model. The drawback of AVMs is that they are, generally speaking, very

difficult for humans to fully understand, although it is possible for humans to see the inputs and assess the

accuracy of an AVM’s outputs. This drawback can be outweighed by the primary benefit of AVMs: that

they can be significantly more accurate than human-interpretable models.

The Appraisal of Real Estate outlines professional standards around the use of an AVM. An

appraiser must be able to answer affirmatively to all five of the following criteria:

1. Does the appraiser have a basic understanding of how the AVM works?

2. Can the appraiser use the AVM properly?

3. Are the AVM and the data it uses appropriate given the intended use of assignment results?

4. Is the AVM output credible?

5. Is the AVM sufficiently reliable for use in the assignment?

As we will show below, we are able to answer affirmatively to all five of these criteria.

The Appraisal of Real Estate is ambivalent about the use of AVMs for appraisal. On the one hand,

the book suggests that “the output of an AVM is not, by itself, an appraisal” and suggests that the AVM

should be used as the basis of a professional opinion if it is credible. Our use of an AVM follows these

guidelines. On the other hand, The Appraisal of Real Estate also recognizes that our specific use case in

this report, “property tax assessment and equity studies”, is a valid application for AVMs.

The Appraisal of Real Estate also writes that: “Appraisers should not attempt to build a statistical

model that is beyond the limits of their education and experience. As with any appraisal specialty, the

ability to address complex statistical problems grows with experience. Experience is best gained by


collaboration with a more qualified statistical analyst”. Following this recommendation, the Property Tax

Consultant preparing this report was assisted in the construction of an AVM by PXN Residential LLC, a

real-estate software company whose principals are faculty members at the Wharton School (University of

Pennsylvania) and the University of California, Berkeley, and who have deep academic and practical

experience in the construction of machine learning models for predictive analytics.

Results with the AVM. One of the strengths of the AVM is that the price of the subject property

on January 1, 2020 can be precisely estimated by inputting the characteristics of 2510 Rosewood Ave into

the model and setting a (hypothetical) sale date of January 1, 2020. When we did that, the AVM produced a

guessed sale price of $600,862.

Professional Standards Test with our AVM. Our use of the AVM meets the five-prong test

established by the Appraisal Institute.

1. Does the appraiser have a basic understanding of how the AVM works?

Yes. The AVM creates a regression model to predict the sale price for all single-family properties

with between 2 and 4 bedrooms in the 78702 ZIP code with square footage within 25% of 2510

Rosewood Ave and lot square footage within 50% of 2510 Rosewood Ave within 2 miles of 2510

Rosewood Ave. The model only considers those homes sold in the 36 months prior to January 1,

2020.

2. Can the appraiser use the AVM properly?

Yes. This AVM is designed for the prediction of sale prices. It takes as input the attributes of the

subject property and outputs a sale price.

3. Are the AVM and the data it uses appropriate given the intended use of assignment results?

Yes. The data considered by the model includes the ZIP code, the number of bedrooms, the number

of bathrooms, the size of the property, the location of the property as well as the date of the sale.

4. Is the AVM output credible?

Yes. The results of the AVM are shown graphically as Figure 4. Each dot represents one of the 64

relevant sold homes; the dot is placed by plotting the actual sale price against the AVM’s predicted

sale price. The blue line represents perfect predictions, where the guessed prices exactly match the


model. The dots cluster around the blue line, indicating an excellent model fit of the input data. The

large red star in the center of the plot represents the AVM’s valuation of 2510 Rosewood Ave.

Figure 4. A comparison of guessed sale prices to actual sale prices in our site-specific AVM. Each black

dot represents a relevant sale and is plotted based on the sale price from our model and its actual sale price.

The blue line represents perfect accuracy, wherein the model’s prices would exactly predict sale prices. The

model’s estimate of the price of 2510 Rosewood Ave on January 1, 2020 is shown by the red star.

An important quantitative characteristic for assessing the credibility of the AVM is error. The error

is the average amount that the model either overpredicts or underpredicts the real sale price. For

instance, if the model guesses a sale price of $350,000 for a home and the home actually sold for

$355,000, then there is an error of $5,000 associated with that prediction. The AVM has an average

error of $2,210 for the homes featured in Figure 4. This represents less than 1% of the model’s

valuation of 2510 Rosewood Ave. Given these error rates, the site-specific AVM we developed here

is both appropriate for use in estimating the price of 2510 Rosewood Ave and is significantly more

accurate than Zillow’s Zestimate AVM.

5. Is the AVM sufficiently reliable for use in the assignment?


Yes. A quantitive measure of the reliability of an AVM is known as bias. A biased model

consistently guesses too high or too low in its home price predictions. For the homes featured in

Figure 4, the AVM has a bias of $15, meaning that, on average, it guesses that home prices are $15

less than the actual sale price. Since the average sale price of homes in Figure 4 is much larger than

this value, we can safely say that the AVM is essentially unbiased, and therefore sufficiently reliable

for use in this assignment.

Regarding Zillow’s Zestimate. Zillow’s Zestimate is the best-known AVM. However, we do not

consider using Zestimate for 2510 Rosewood Ave because it suffers from two problems.

First, because of data fragmentation in the State of Texas, Zillow does not have enough data to

accurately value properties in Travis County. Zillow’s own corporate communications rate Zestimate’s

accuracy in Travis County as just “one star” out of four (where two stars are “Fair” quality, and three stars

are “Good” quality), and moreover, Zillow is unwilling to attest to the accuracy of Zestimates in Travis

County. Zillow generally reports Zestimate errors (how close actual sale prices are to Zestimates) for most

counties across the USA. But in Travis County, Zillow does not report an average error for Zestimates

versus actual sale prices. On a nationwide basis Zillow claims that Zestimate is typically 1.9% away from a

home’s true value, but that error is likely to be higher in Travis County. Zillow’s own communications

describe Zestimate accuracy as worse than “Fair” in Travis County, but in Tarrant County, where the

Zestimate accuracy is listed as “Fair”, Zestimate’s typical error is 7.4%. This suggests the error of typical

Zestimates in Travis County is very likely to be higher than 8%.1

The second reason we do not use Zestimate is that it is necessarily less specific to properties in

Travis County. A Zestimate has a very broad job: it is designed to produce price predictions for every

home in every county across the USA. Instead, for our purposes it is possible to build a more precise AVM

specific to 2510 Rosewood Ave.

1 All information on the accuracy of Zestimates in the state of Texas is publicly available at:

https://www.zillow.com/howto/DataCoverageZestimateAccuracyTX.htm


Disclaimer

While care has been taken to present accurate data, neither Bob McKenna, Austin Real Estate

Partners, nor PXN Residential LLC can guarantee the accuracy of the data used in this report.

Certain data in this report is Copyright 2020 Austin Central Texas Real Estate Information Services,

Inc (ACTRIS). This report is based on information from the Austin Board of REALTORS (R)

(alternatively, from ACTRIS) for the period January 1, 2015 to January 1, 2020. Neither the Board nor

ACTRIS guarantees or is any way responsible for its accuracy. All data is provided ‘AS IS’ and with all

faults. Data maintained by the Board or ACTRIS may not reflect all real estate activity in the market.

prepared by bob mckenna licensed property tax consultant · 2020-04-16 · bedroom count 3.0 3.0 $0...

Documents