The modern real estate market is full of ebbs and flows. Predicted textbook market cycles seem to be a thing of the past. Suddenly, those typical seasonal surges and falls seem to exist within a very short timeline.
In expected growing markets, price movement can involve a more measured response, but in less than anticipated markets, there isn’t always a sense of right from wrong in terms of pricing.
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Much of this has to do with supply issues which can inhibit early stages of positivity.
Oversupply drags prices down, where under supply can drive prices up. Predicting this market is now the job of machine learning and collected data.
Let’s take a look at how data is changing real estate brick-by-brick.
This often hinges on an agent’s overall knowledge and expertise in the local neighborhood.
When setting a price, similar homes are considered and any add-on amenities are factored in to help establish a fair price.
While all of that can help to craft the perfect asking price, utilizing machine learning solutions development and data can prove a more strategic approach.
With machine learning, market trends can be tracked all while current supply and past sales are fairly considered.
Property features (size, quality of materials, unique amenities, number of bedrooms) can also be analyzed to set a price.
The right listing price could translate to a quick sale and a successful transaction.
A price that’s too low or too high could leave a property stagnant.
Unfortunately, selling and marketing these properties is often easier said than done.
After all, no one wants to sit on a luxury property for months or years waiting for the turnaround.
In the past, real estate agents simply targeted a few affluent buyers worldwide.
However, many agents are finding that AI-powered brokerage and machine learning can market properties better than agents themselves.
With AI, listings can be marketed to individuals who speak different languages and those who live on different continents.
At the end of the day, machines don’t care where a buyer lives or where they’re from, it is all about getting the sale.
For example, in a bustling New York City real estate market, data can help to garner detailed knowledge about properties, ranging from how noisy a neighborhood is going to be to how dangerous nearby intersections are.
Given that a whopping 40% of all home buyers regret their purchase after just two years, utilizing data to give buyers an expanded look of what life will be like at a given property is paramount.
Without data and machine learning capabilities, it wouldn’t be possible to paint such a vivid picture or yield statistics on life in a given neighborhood.
From pricing properties to engaging potential buyers, there are numerous reasons why data can help real estate agents daily in the current market.
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In expected growing markets, price movement can involve a more measured response, but in less than anticipated markets, there isn’t always a sense of right from wrong in terms of pricing.
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| How Data Is Changing Real Estate Brick-by-Brick: eAskme |
Much of this has to do with supply issues which can inhibit early stages of positivity.
Oversupply drags prices down, where under supply can drive prices up. Predicting this market is now the job of machine learning and collected data.
Let’s take a look at how data is changing real estate brick-by-brick.
Establishing Home Prices
One of the most crucial aspects of real estate is setting an asking price.This often hinges on an agent’s overall knowledge and expertise in the local neighborhood.
When setting a price, similar homes are considered and any add-on amenities are factored in to help establish a fair price.
While all of that can help to craft the perfect asking price, utilizing machine learning solutions development and data can prove a more strategic approach.
With machine learning, market trends can be tracked all while current supply and past sales are fairly considered.
Property features (size, quality of materials, unique amenities, number of bedrooms) can also be analyzed to set a price.
The right listing price could translate to a quick sale and a successful transaction.
A price that’s too low or too high could leave a property stagnant.
Selling and Marketing A Property
Real estate agents yearn to sell and market high-end properties that can yield a large commission.Unfortunately, selling and marketing these properties is often easier said than done.
After all, no one wants to sit on a luxury property for months or years waiting for the turnaround.
In the past, real estate agents simply targeted a few affluent buyers worldwide.
However, many agents are finding that AI-powered brokerage and machine learning can market properties better than agents themselves.
With AI, listings can be marketed to individuals who speak different languages and those who live on different continents.
At the end of the day, machines don’t care where a buyer lives or where they’re from, it is all about getting the sale.
Key Facts About Properties
Using machine learning real estate agents can combine both commercial and public data to garner valuable insights.For example, in a bustling New York City real estate market, data can help to garner detailed knowledge about properties, ranging from how noisy a neighborhood is going to be to how dangerous nearby intersections are.
Given that a whopping 40% of all home buyers regret their purchase after just two years, utilizing data to give buyers an expanded look of what life will be like at a given property is paramount.
Without data and machine learning capabilities, it wouldn’t be possible to paint such a vivid picture or yield statistics on life in a given neighborhood.
The Takeaway
While most of us think of machine learning and data as being useful in tech and retail sectors, real estate benefits in a huge way from utilizing big data.From pricing properties to engaging potential buyers, there are numerous reasons why data can help real estate agents daily in the current market.
If you find this article interesting, don’t forget to share it with your friends and family.
Sharing is Caring!
Don't forget to like us FB and join the eAskme newsletter to stay tuned with us.
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