Your AI Navigator in Commercial Real Estate
Sign Up Successful!
You now have access to more from Realmo!
Property Lookup
    Property Lookup
    • For Sale
    • For Lease
    • Recently Sold
    • Property Lookup
    • Brokers
    AI Search
    Back to Search
    Property profile & analytics

    715 College Ave Old Town, ME 04468

    • Vacant Commercial
    • 4.80 lot acres
    OFF-MARKET
    Value Estimation
    Location Intelligence
    Property ID
    US42-0340560
    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

    • Home of Jason n Lauren Horne / Housing Society

    Property Details for 715 College Ave

    Lot size
    4.80 lot acres
    Property type
    Vacant Commercial
    APN
    OLDN M:017 L:013-00A
    APU
    OLDNM017L01300A

    Value Estimation

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

    • CAP approach

    • Comparable approach

    • Alternative Use

    Location Insight

    • Map
    • Local Demand
    City
    Old Town
    County
    Penobscot
    State
    Maine
    Longitude
    -68.685526
    Latitude
    44.914663
    Request Owner's contact information
    Message sent successfully
    Error
    If you are the owner or broker of this property and would like to edit or remove this listing please send a message to web site administrator.
    715 College Ave
    Old Town, ME 04468
    Message sent successfully
    Error
    Email to Your Friends
    Link copied to clipboard
    Separate multiple addresses with a comma
    Message sent successfully
    Error

    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
    Thanks! Your message was sent.
    Error! Your message wasn't sent.
    Please enter your name
    Please enter email
    Please enter the email in the correct format
    Please enter phone
    Please enter the number in the correct format
    Please enter message
    Google sign-in failed. Please try again or use another login method.
    Create New Password
    • At least 8 characters
    • Contains both letters and numbers
    • Contains uppercase and lowercase letters
    • At most 32 characters
    • Password required
    • Password reset link has expired. Please request a new one.