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How Ex-Data Analyst Nisha Patel Uses Property History to Find BMV Deals

How Ex-Data Analyst Nisha Patel Uses Property History to Find BMV Deals

How Ex-Data Analyst Nisha Patel Uses Property History to Find BMV Deals

How Ex-Data Analyst Nisha Patel Uses Property History to Find BMV Deals

Property Filter logo featuring a blue brick circle icon with three tilted property filter symbols, next to bold blue text reading 'PROPERTY FILTER'
Property Filter logo featuring a blue brick circle icon with three tilted property filter symbols, next to bold blue text reading 'PROPERTY FILTER'
Nisha Patel - ex-data analyst and property investor using Property Filter

Negotiated £50K below asking price using Property Filter

Nisha Patel · Property Investor · Ex-Data Analyst · Interviewed September 2025

AT A GLANCE

Member

Nisha Patel

Company

Independent Property Investor

Background

18 years as a data analyst

Deal 1

Tracked in pipeline for 1 year → negotiated £50K below asking

Deal 2

Listed £325K → purchased £275K (£50K BMV) → 218 sqm property → future 8-bed HMO

PF Feature Used

Property history (listing history, price changes, fall-throughs, withdrawals)

"Property Filter is making those deals. It's getting all that data already there."

Nisha Patel, Property Investor

Results like these are happening every week. See how Property Filter works.

Nisha Patel spent 18 years as a data analyst before turning to property. When she discovered that Property Filter's motivated seller data included full listing history - price changes, fall-throughs, withdrawals - her reaction was immediate: "Oh my God, this is data." She has since used that history to negotiate £50,000 below asking price on two separate deals.

The Data Analyst Who Recognised PF Immediately

Nisha Patel came to property with a different set of instincts to most investors. After 18 years working in data - building formulas, macros, and systems to make information usable - she could not find a property research process that felt remotely efficient. So she built one.

Her system involved a VA researching each property individually, cross-referencing data from multiple portals, and compiling it into elaborate spreadsheets. Each property took more than an hour to assess. The process worked, but at a speed that made scaling genuinely difficult.

She first encountered Property Filter when Guillaume, co-founder of Property Filter, presented it at a property meeting. She subscribed on the spot - no hesitation, no trial period, straight to the annual plan. The reason was simple: she recognised the quality of the data immediately. As she put it: "Oh my God, this is data."

The Feature That Changed Everything

Nisha's favourite Property Filter feature is property history - and it is easy to understand why. On Rightmove or Zoopla, you see today's listing. You see the current price and the current description. You do not see how long the property has actually been available, whether the price has been cut, whether it went under offer and fell through, or whether it was withdrawn entirely and relisted with a different agent.

"I needed to know if that property was worth it or not before I do the viewing, not after."

Property Filter shows all of that history. Every price change. Every status movement. Every fall-through. Nisha used to pay a VA to piece together this picture from fragments across different portals. Now it is in one view, inside Property Filter, before she has made a single phone call.

Nisha Patel spent 18 years as a data analyst before turning to property. When she discovered that Property Filter's motivated seller data included full listing history - price changes, fall-throughs, withdrawals - her reaction was immediate: "Oh my God, this is data." She has since used that history to negotiate £50,000 below asking price on two separate deals.

The Data Analyst Who Recognised PF Immediately

Nisha Patel came to property with a different set of instincts to most investors. After 18 years working in data - building formulas, macros, and systems to make information usable - she could not find a property research process that felt remotely efficient. So she built one.

Her system involved a VA researching each property individually, cross-referencing data from multiple portals, and compiling it into elaborate spreadsheets. Each property took more than an hour to assess. The process worked, but at a speed that made scaling genuinely difficult.

She first encountered Property Filter when Guillaume, co-founder of Property Filter, presented it at a property meeting. She subscribed on the spot - no hesitation, no trial period, straight to the annual plan. The reason was simple: she recognised the quality of the data immediately. As she put it: "Oh my God, this is data."

The Feature That Changed Everything

Nisha's favourite Property Filter feature is property history - and it is easy to understand why. On Rightmove or Zoopla, you see today's listing. You see the current price and the current description. You do not see how long the property has actually been available, whether the price has been cut, whether it went under offer and fell through, or whether it was withdrawn entirely and relisted with a different agent.

"I needed to know if that property was worth it or not before I do the viewing, not after."

Property Filter shows all of that history. Every price change. Every status movement. Every fall-through. Nisha used to pay a VA to piece together this picture from fragments across different portals. Now it is in one view, inside Property Filter, before she has made a single phone call.

Feature Spotlight

Property History: The Full Listing Timeline

Property Filter's listing history view shows the complete journey of a property on the market: every price change, every status transition (listed, under offer, SSTC, withdrawn, relisted), and every fall-through. Most portals show only the current listing. Property Filter shows everything that happened before it.

For a data analyst like Nisha, this is the difference between making an offer based on what a seller is asking today - and making an offer based on what a seller has been through over the past six months. A property that has dropped in price twice, gone under offer and fallen through once, and been relisted with a new agent is telling you something about motivation that no portal currently shows. Property Filter does.

Property History: The Full Listing Timeline

Property Filter's listing history view shows the complete journey of a property on the market: every price change, every status transition (listed, under offer, SSTC, withdrawn, relisted), and every fall-through. Most portals show only the current listing. Property Filter shows everything that happened before it.

For a data analyst like Nisha, this is the difference between making an offer based on what a seller is asking today - and making an offer based on what a seller has been through over the past six months. A property that has dropped in price twice, gone under offer and fallen through once, and been relisted with a new agent is telling you something about motivation that no portal currently shows. Property Filter does.

The Year-Long Pipeline Deal

Nisha's first major deal using Property Filter took patience. She tracked a property in PF's pipeline for a full year - monitoring its price history, watching for status changes, and waiting for the seller's position to shift. When the moment came, she was ready. She negotiated £50,000 below the asking price. The year spent watching in the pipeline had given her the context to make an offer the seller could not reasonably decline.

That deal would not have been possible with a standard portal search. You cannot track a property's history across 12 months on Rightmove. You can watch it disappear and reappear, but without the data to understand why, the history has no meaning. Property Filter makes the history legible.

The 218-Square-Metre Find

Her most recent deal is the one with the largest future potential. The property was listed at £325,000. Using Property Filter's history data to understand the seller's position, Nisha negotiated the purchase to £275,000 - £50,000 below the asking price.

What makes the deal particularly significant is the property itself: a 218-square-metre terraced house. That is a very large footprint for a terrace. While she is currently holding it as a rental, her plan is to seek planning permission for a conversion to an eight-bedroom HMO. If approved, the scale of the property makes that an exceptional opportunity - one that started with a Property Filter history search.

From Letter Campaigns to Built-In Outreach

In 2021, Nisha ran direct letter campaigns to motivated sellers - and they worked. She no longer does this, not because it stopped working, but because Property Filter's built-in letter-sending functionality now handles the outreach directly from within the platform. The manual process she built herself has become a native feature.

That progression reflects something important about how Nisha approaches property. She builds systems because that is how she thinks - as a data professional, efficiency is instinctive. Property Filter aligned with that thinking from day one, and has continued to develop in directions that match how she actually works.

To see how property sourcing software like Property Filter surfaces listing history, price changes, and seller motivation data before a single viewing is booked, watch the Property Filter demo.

Watch Nisha's Full Story

The Year-Long Pipeline Deal

Nisha's first major deal using Property Filter took patience. She tracked a property in PF's pipeline for a full year - monitoring its price history, watching for status changes, and waiting for the seller's position to shift. When the moment came, she was ready. She negotiated £50,000 below the asking price. The year spent watching in the pipeline had given her the context to make an offer the seller could not reasonably decline.

That deal would not have been possible with a standard portal search. You cannot track a property's history across 12 months on Rightmove. You can watch it disappear and reappear, but without the data to understand why, the history has no meaning. Property Filter makes the history legible.

The 218-Square-Metre Find

Her most recent deal is the one with the largest future potential. The property was listed at £325,000. Using Property Filter's history data to understand the seller's position, Nisha negotiated the purchase to £275,000 - £50,000 below the asking price.

What makes the deal particularly significant is the property itself: a 218-square-metre terraced house. That is a very large footprint for a terrace. While she is currently holding it as a rental, her plan is to seek planning permission for a conversion to an eight-bedroom HMO. If approved, the scale of the property makes that an exceptional opportunity - one that started with a Property Filter history search.

From Letter Campaigns to Built-In Outreach

In 2021, Nisha ran direct letter campaigns to motivated sellers - and they worked. She no longer does this, not because it stopped working, but because Property Filter's built-in letter-sending functionality now handles the outreach directly from within the platform. The manual process she built herself has become a native feature.

That progression reflects something important about how Nisha approaches property. She builds systems because that is how she thinks - as a data professional, efficiency is instinctive. Property Filter aligned with that thinking from day one, and has continued to develop in directions that match how she actually works.

To see how property sourcing software like Property Filter surfaces listing history, price changes, and seller motivation data before a single viewing is booked, watch the Property Filter demo.

Watch Nisha's Full Story

Frequently asked questions

Frequently asked questions

How does Nisha Patel use Property Filter's property history feature?

Deal 1: Nisha tracked a property in Property Filter's pipeline for a full year - monitoring its history and price movements - before negotiating £50,000 below the asking price. Deal 2: A 218-square-metre terraced house listed at £325,000, which she purchased for £275,000 (£50,000 BMV). She is currently holding it as a rental while seeking planning permission to convert it to an 8-bed HMO.

What were Nisha Patel's two BMV deals using Property Filter?

Deal 1: Nisha tracked a property in Property Filter's pipeline for a full year - monitoring its history and price movements - before negotiating £50,000 below the asking price. Deal 2: A 218-square-metre terraced house listed at £325,000, which she purchased for £275,000 (£50,000 BMV). She is currently holding it as a rental while seeking planning permission to convert it to an 8-bed HMO.

Why did Nisha Patel switch from spreadsheets to Property Filter?

As a former data analyst with 18 years in the data industry, Nisha built elaborate spreadsheets and hired a VA to research properties manually - with each property taking over an hour to assess. She switched to Property Filter after seeing it demonstrated and immediately recognising the data quality: "Oh my God, this is data." Property Filter condensed her research process from hours to minutes.

How did Nisha Patel use letter campaigns with Property Filter?

In 2021, Nisha ran letter campaigns to reach motivated sellers directly - and they worked. She no longer does this manually because Property Filter now includes built-in letter-sending functionality, which integrates outreach directly with motivated seller search results. The process Nisha built manually is now a native feature within the platform.

What is the difference between Property Filter and Rightmove for property research?

On Rightmove, you see only today's listing - the current price, the current description. Property Filter shows the full history: how long the property has actually been on the market, every price change, whether it went under offer and fell through, and whether it was withdrawn and relisted. That history is what reveals seller motivation - and it is not available on any standard portal.

See the Property History Data That Reveals Motivated Sellers

See the Property History Data That Reveals Motivated Sellers

See the Property History Data That Reveals Motivated Sellers

Price changes, fall-throughs, withdrawals - in one view, before you book a viewing.

Price changes, fall-throughs, withdrawals - in one view, before you book a viewing.

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