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This is a live AVM report that will continually update over time.

Automated valuation model report

Property information

Address6 1744 7 Street SW
LocationCalgary, Alberta, Canada
Coordinates51.036118, -114.079201
Interior Size109 m² (1171 ft²)
Property TypeHouse
Bedrooms3
Den0
Full Bathrooms2
Half Bathrooms0
Year Built2008
Property

Valuation estimates

AVM valuation date 2025-07-25

Estimated list price

$924,645

Confidence95.4%

Estimated sale price

$1,107,127

Confidence95.3%

Estimated monthly rent

$2,727

Confidence89.3%

Model configuration & statistics

Sale price model

Training R-squared0.980
Testing R-squared0.950
Mean Absolute Error$42,485
RMSE$86,201
MAPE6.9%
Median MAPE4.7%
Within 5%52.1%
Within 10%79.6%
Within 15%90.6%
Within 20%95.2%
Mean Bias-$2,038
Median Bias$1,074
Over-prediction Rate51.5%
Under-prediction Rate48.5%
10th Percentile Error$3,737
25th Percentile Error$9,773
50th Percentile Error$22,599
75th Percentile Error$46,562
90th Percentile Error$90,114
95th Percentile Error$143,924
10th Percentile % Error0.8%
90th Percentile % Error14.5%
Forecast Std Dev$86,177
Coefficient of Variation15.073

List price model

Training R-squared0.990
Testing R-squared0.950
Mean Absolute Error$43,213
RMSE$88,259
MAPE6.8%
Median MAPE4.6%
Within 5%52.9%
Within 10%80.1%
Within 15%91.0%
Within 20%95.1%
Mean Bias-$1,991
Median Bias$1,133
Over-prediction Rate51.5%
Under-prediction Rate48.5%
10th Percentile Error$3,801
25th Percentile Error$9,678
50th Percentile Error$22,613
75th Percentile Error$46,920
90th Percentile Error$92,493
95th Percentile Error$145,700
10th Percentile % Error0.8%
90th Percentile % Error14.4%
Forecast Std Dev$88,236
Coefficient of Variation15.057

Rental price model

Training R-squared0.870
Testing R-squared0.800
Mean Absolute Error$119,685
RMSE$223,691
MAPE18.4%
Median MAPE10.7%
Within 5%26.4%
Within 10%47.5%
Within 15%61.9%
Within 20%71.7%
Mean Bias-$23,911
Median Bias$3,580
Over-prediction Rate51.9%
Under-prediction Rate48.1%
10th Percentile Error$9,805
25th Percentile Error$25,677
50th Percentile Error$61,443
75th Percentile Error$133,961
90th Percentile Error$271,068
95th Percentile Error$419,132
10th Percentile % Error1.8%
90th Percentile % Error41.5%
Forecast Std Dev$222,409
Coefficient of Variation32.419

Comparable properties

First 10 properties of 75,192 comparable properties analyzed
AddressDistanceList PriceSale Date (Estimated)
301 823 19 Avenue SW0.11 km$259,9002025-04-28
405 824 Royal Avenue SW0.12 km$485,0002025-07-16
308 836 Royal Avenue SW0.15 km$485,0002025-05-24
408 823 Royal Avenue SW0.18 km$349,9002025-04-16
2102 7 Street SW0.23 km$1,699,0002025-05-16
101 910 18 Avenue SW0.25 km$390,0002025-05-16
203 1730 5A Street SW0.29 km$519,0002025-05-24
408 1501 6 Street SW0.32 km$304,9002025-07-23
1106 1501 6 Street SW0.32 km$309,9002025-07-04
305 545 18 Avenue SW0.39 km$324,9002025-07-04

Glossary of terms

AVM (Automated Valuation Model)

A computer program that uses data and statistical methods to estimate property values without human input.

Confidence Score

A percentage showing how reliable the model thinks its estimate is. Higher percentages mean the model is more confident.

$$\text{Confidence Score} = 100 - \text{Median MAPE}$$

The confidence score is calculated by subtracting the model's median MAPE from 100%. For example, if the median MAPE is 8%, the confidence score would be 92%. This means the model is typically accurate within 8% of the actual value, so we can be 92% confident in its predictions.

Training vs Testing Data

The model learns from training data, then its accuracy is tested on separate data it hasn't seen before. Testing results are more reliable indicators of real-world performance.

R-squared (Coefficient of Determination)

A measure of how well the model explains property values. Values range from 0 to 1, with values closer to 1.0 meaning the model is better at predicting prices.

$$R^2 = 1 - \frac{SS_{res}}{SS_{tot}}$$

Where SSres is the sum of squares of residuals and SStot is the total sum of squares.

Mean Absolute Error (MAE)

The average difference between the model's predictions and actual sale prices. Lower values mean more accurate predictions.

$$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$$

Where yi is the actual value, ŷi is the predicted value, and n is the number of predictions.

Root Mean Square Error (RMSE)

Similar to MAE but gives more weight to larger errors. Lower values indicate better accuracy.

$$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$$

The square root of the average of squared differences between actual and predicted values.

MAPE (Mean Absolute Percentage Error)

The average percentage difference between predicted and actual values. Lower percentages mean better accuracy.

$$MAPE = \frac{1}{n} \sum_{i=1}^{n} \left|\frac{y_i - \hat{y}_i}{y_i}\right| \times 100\%$$

Shows error as a percentage of the actual value, making it easier to interpret across different price ranges.

Median MAPE

The middle value when all percentage errors are sorted from lowest to highest. Less affected by extreme errors than regular MAPE.

Often provides a better sense of typical prediction accuracy than the mean.

Within X% Accuracy

The percentage of predictions that were within X% of the actual sale price. Higher percentages are better.

$$\text{Within X%} = \frac{\text{Count of } |\text{error}| \leq X\% \text{ of actual}}{\text{Total predictions}} \times 100\%$$

For example, 'Within 10%' means what percentage of predictions were within 10% of the actual sale price.

Mean Bias

Whether the model tends to predict prices too high (positive bias) or too low (negative bias) on average. Values closer to zero are better.

$$\text{Mean Bias} = \frac{1}{n} \sum_{i=1}^{n} (\hat{y}_i - y_i)$$

Positive values mean the model overestimates, negative values mean it underestimates.

Median Bias

The middle value of all prediction errors when sorted. Less affected by extreme errors than mean bias.

Provides a better sense of typical model behavior than mean bias.

Over-prediction Rate

The percentage of times the model predicted higher than the actual sale price.

$$\text{Over-prediction Rate} = \frac{\text{Count of } \hat{y}_i > y_i}{\text{Total predictions}} \times 100\%$$

A well-calibrated model should have an over-prediction rate close to 50%.

Under-prediction Rate

The percentage of times the model predicted lower than the actual sale price.

$$\text{Under-prediction Rate} = \frac{\text{Count of } \hat{y}_i < y_i}{\text{Total predictions}} \times 100\%$$

Should complement the over-prediction rate (both should sum to ~100%).

Percentile Error

Shows the range of prediction errors. For example, '90th percentile error' means 90% of predictions were more accurate than this amount.

Lower percentiles (10th, 25th) show the model's best performance, while higher percentiles (90th, 95th) show its worst cases.

Percentile % Error

Similar to percentile error but expressed as a percentage of the actual value.

Makes it easier to compare error ranges across different price levels.

Forecast Standard Deviation

A measure of how much the model's predictions typically vary from the average prediction. Lower values indicate more consistent predictions.

$$\sigma = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (\hat{y}_i - \bar{y})^2}$$

Where ȳ is the average of all predictions.

Coefficient of Variation

A measure of prediction consistency relative to the average value. Lower values mean more consistent predictions.

$$CV = \frac{\text{Standard Deviation}}{\text{Mean}} \times 100$$

Allows comparison of variability across different price ranges.

Comparable Properties

Similar properties in the area that have recently sold, used to help estimate the value of the property.

The model analyzes these properties to understand local market conditions and pricing patterns.

Important information

These valuations are generated by machine learning models using comparable property data, market trends, and location factors. The estimates are based on statistical analysis and should be used as a reference point only.

Model accuracy is measured using historical sales data. Actual market conditions, unique property features, and other factors not captured in the model may affect the true market value.

For investment decisions or property transactions, we strongly recommend obtaining a professional appraisal from a licensed appraiser who can assess the specific characteristics of the property.

This report does not constitute a formal appraisal and should not be used as the sole basis for financial decisions.