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    Property profile & analytics

    16010 Barkers Point Ln Houston, TX 77079

    • Skyscraper/High-Rise
    • 3.03 lot acres
    OFF-MARKET
    Value Estimation
    Location Intelligence
    Property ID
    US82-3122548
    The Realmo ID is a unique identifier assigned to every property, incorporating country codes and precise geo-coordinates for 300x300-foot land blocks. It enables standardized data collection and seamless integration of disparate sources such as ownership, tax, title data and physical specifications. By aggregating current and historical records, the Realmo ID powers advanced analytics and delivers deep, verifiable insights for real estate professionals.

    Current Use by Public Records

    • Javier Tapia / Insurance Agency / Auto Insurance Agency
    • Dimensional Solutions, Inc. / Software Company
    • EVO Flight Services / Transportation Service
    • Farmers Insurance - Donald Mays / Insurance Agency / Auto Insurance Agency
    • Farmers Insurance - Bryce Ritzen / Insurance Agency / Auto Insurance Agency

    Property Details for 16010 Barkers Point Ln

    Year built
    1983
    Lot size
    3.03 lot acres
    Property type
    Skyscraper/High-Rise
    APN
    1142980010006
    APU
    1142980010006

    Value Estimation

    Powered by Realmo proprietary methodology Last update: July 3, 2025

    • CAP approach

    • Comparable approach

    • Alternative Use

    Location Insight

    • Map
    • Local Demand
    City
    Houston
    County
    Fort Bend
    State
    Texas
    Longitude
    -95.642966
    Latitude
    29.777938

    Property Tax & Assessments

    Tax date
    Tax Amount
    Assessed Land Value
    Assessed Improvement Value
    Total Assessed Value
    Applied Tax Rate
    Land Market Value
    Improvement Market Value
    Total Market Value
    0
    $0.00
    $0.00
    $0.00
    $0.00
    -
    $1,484,798
    $0.00
    $9,746,414
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    16010 Barkers Point Ln
    Houston, TX 77079
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    Realmo Value Estimation

    Realmo estimates a property’s market value using a proprietary methodology that incorporates public data and user-submitted information. This cumulative value is generated by integrating a variety of conventional and innovative methods. 

    Disclaimer

    These figures are not official appraisals and should not be used as a substitute for a valuation performed by a licensed appraiser.

    Actual market value may differ due to factors not captured in the data. Estimates are offered for informational purposes only, and users should conduct independent research before making financial or investment decisions.

    By using this feature, you acknowledge that Realmo is not responsible for actions taken based on these estimates.

    AI approach

    Realmo AI Method estimates property value using machine learning models trained on millions of comparable listings, transactions, and market signals. It analyzes property features, location intelligence data, and current market dynamics to predict the most probable market value in real time.

    Cap approach

    Capitalization Method calculates value based on estimated net operating income (NOI) and applying a market-based capitalization rate (cap rate), we estimate what a typical investor might be willing to pay for it.

    Comparable approach

    Comparable estimate is based on recent sales of similar nearby properties, adjusted for factors like size, condition, and location.

    Relative

    Relative property class coefficient uses factors that account for typical price differences between various asset types (e.g., retail vs. industrial) to provide a more accurate market-aligned estimate.

    Geo

    Comparable Sales Method, enhanced by Geo coefficients — location-based adjustment factors that reflect differences in price levels across neighborhoods, cities, or regions ensuring a more accurate, market-sensitive valuation.

    Alternative use

    Estimate considers the property’s potential value if used for a different purpose-based on zoning, demand, and market feasibility.

    More about this property
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