Detect sales chasing in a vector of sales ratios#

assesspy.detect_chasing(ratio, method='both')#

Sales chasing is when a property is selectively reappraised to shift its assessed value toward its actual sale price. Sales chasing is difficult to detect. This function is NOT a statistical test and does not provide the probability of the given result. Rather, it combines two novel methods to roughly estimate if sales chasing has occurred.

The first method (dist) uses the technique outlined in the IAAO Standard on Ratio Studies Appendix E, Section 4. It compares the percentage of real data within +-2% of the mean ratio to the percentage of data within the same bounds given a constructed normal distribution with the same mean and standard deviation. The intuition here is that ratios that are sales chased may be more “bunched up” in the center of the distribution.

The second method (cdf) detects discontinuities in the cumulative distribution function (CDF) of the input vector. Ratios that are not sales chased should have a fairly smooth CDF. Discontinuous jumps in the CDF, particularly around 1, may indicate sales chasing. This can usually be seen visually as a “flat spot” on the CDF.

Parameters:

ratio (numeric) – A numeric vector of ratios centered around 1, where the numerator of the ratio is the estimated fair market value and the denominator is the actual sale price.
method (str) – Default “both”. String indicating sales chasing detection method. Options are cdf, dist, or both.

Returns:

A logical value indicating whether or not the input ratios may have been sales chased.

Return type:

bool

Example:

import assesspy as ap
import numpy as np
from statsmodels.distributions.empirical_distribution import ECDF
from matplotlib import pyplot

# Generate fake data with normal vs chased ratios
normal_ratios = np.random.normal(1, 0.15, 10000)
chased_ratios = list(np.random.normal(1, 0.15, 900)) + [1] * 100

# Plot to view discontinuity
ecdf = ECDF(normal_ratios)
pyplot.plot(ecdf.x, ecdf.y)
pyplot.show()
ap.detect_chasing(normal_ratios)

ecdf = ECDF(chased_ratios)
pyplot.plot(ecdf.x, ecdf.y)
pyplot.show()
ap.detect_chasing(chased_ratios)