Ep. 269: The Danger of Property Filters in Analysing Data, with Kent Lardner

In this week’s episode of the Geared for Growth podcast, host Mike Mortlock is joined once again by Kent Lardner, Property Data Analyst and Founder of SuburbTrends, to explore the challenges of relying on common filters when analysing property data.

Kent dives into commonly used filters like median price, rental tenure, and vacancy rates, explaining how these can lead to misleading outcomes. He emphasises that just one rejected filter can exclude entire suburbs that might have otherwise been excellent investment opportunities.

To provide deeper insight, Kent and Mike discuss a range of case studies that highlight how filters can distort results. Kent outlines his preferred methods for data analysis, stressing the importance of distinguishing between property-level and suburb-level metrics for a more accurate evaluation.

The conversation wraps up with an analysis of Automated Valuation Models (AVMs), weighing their potential benefits, such as unlocking equity, against the risks, including fraudulent borrowing.

This episode provides a detailed and practical guide to refining property investment strategies, helping listeners make better use of the wealth of online data available today.

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In this week’s episode of the Geared for Growth podcast, host Mike Mortlock is joined once again by Kent Lardner, Property Data Analyst and Founder of SuburbTrends, to explore the challenges of relying on common filters when analysing property data.

• The danger of using filters to analyse property data
• Breaking down the flaws with common filters
• The problem with vacancy rates and auction clearance rates
• A case study on median price growth
• Understanding the volatility of data
• The biggest risk in using Automated Valuation Models (AVM)

“You put a median price filter on so that’s the given, that’s the first one most people use and it ignores the fact that that’s just the middle value and it’s the middle value at a point in time” 4:25

“If you look at days on market and you look at discounting, they’re fairly volatile in their own right so forget about how they’re measured, just look at what they are and they are very volatile” 18:04

“I believe you’ve got better metrics that can be a catch all for market movement. I like inventory as an example very stock standard measure used in America, great way to aggregate by region, great way to compare regions it doesn’t really matter if there are expensive and cheaper properties” 18:52

“You want to look at those macro things, livability, you want to look at those economic factors such as job diversification, so can I access a university, are there good schools, is it safe, what’s the social economics, is it too far away from a city” 23:11

“You know you’re buying the house, you’re buying the right house in the right suburb and you’re going to ignore so many properties if you stick to that silly suburb level filter approach” 31:44

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