Guide-linked quiz · Medium Level
AI in Real Estate Quiz
Assess real-estate AI by separating estimates from judgments and reviewing data, proxy risks, market segments, privacy, and oversight.
Question 1 of 8
A model estimates a property’s value for internal planning. What should not be assumed?
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- A model estimates a property’s value for internal planning. What should not be assumed?
- That the estimate is a neutral appraisal or appropriate for every decision
- That it has a date
- That it used property data
- That it needs documentation
- Why define the property, market, and date before evaluating an estimate?
- Every property uses the same reference market
- Value depends on market context and the decision being made
- A date changes only the display format
- The model always predicts sale price
- A model uses postal code because it predicts historical price well. What should reviewers consider?
- Whether the code has five digits
- Only whether it improves average error
- Whether it proxies for protected characteristics or historical segregation
- Whether the model is explainable by name
- Why evaluate errors across neighborhoods and property types?
- It eliminates interest-rate changes
- All groups have identical data quality
- The average guarantees fairness
- A citywide average can hide systematic differences
- What should be monitored after deployment?
- Only the vendor’s marketing page
- Listings, interest rates, and data coverage changes
- The model’s logo
- A single citywide score
- What data does the real-estate guide say to protect?
- Only the model’s weights
- Only public house styles
- Applicant, tenant, owner, and location information
- The office phone number
- A property recommendation may steer or exclude applicants. What does the guide recommend?
- Provide oversight and a route to correct inaccurate data
- Let the score silently decide
- Remove the correction path
- Use the same threshold without review
- Before relying on an automated housing outcome, what should a practitioner do?
- Skip privacy review
- Trust a high score alone
- Assume the same rules apply everywhere
- Document the model, data, vendor, threshold, and human action and consult qualified experts
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