What is data science & Big data exactly?
Starting with the fact that more and more data has been generated every day by individuals around the world fuelling to account the term Big Data (BD). The ability to collect, store and analyse large amounts of data to find insights denotes BD analytics as mentioned by Marr (2016). Every organisation especially big companies and government organisations would use large volumes of data where BD solutions will be needed to discover the insights and improve the services they offer.
The clearest definition of data science is one that is found on many websites, which is that data science is ‘the art of uncovering insight and extracting knowledge from data which can be beneficial for business to run efficiently.
The concept of data science is not something that is new, computer scientists and mathematicians have long been studying data. What is new is the coining of the job title Data Scientist which defines the process of making sense of the huge amounts of data that we can now have at our fingertips.
The data science field comprises mathematics, statistics and computer science disciplines, and incorporates techniques like machine learning, deep learning, cluster analysis, data mining and visualisation.
With data science allowing businesses to understand trends and different behaviour types in relation to their clients, it is unsurprising that businesses from a whole range of sectors have been keen to develop their data science capabilities. As a result of machine learning for example, TV streaming services are truly able to analyse the behaviour of their users. Using user’s search history, the age and sex categories that they fit into, the similarities between videos that they watch and those available and comments left by subscribers, TV streaming sites are able to deduce what their subscribers are interested in and offer them targeted and relevant suggestions.
In terms of online shopping, having enough information which corresponds to that of another user (age, sex, location, products bought etc.) means that online shopping websites can offer users products which have been bought by other users that they might not have seen.
In what form does this data come in?
Today, we have a wealth of data available to us. This data can be presented in different forms including ‘structured’ data, meaning it is highly organised and means searching for specific entries is straight forward, as would be found in a database for example. Other types include ‘semi-structured’ and ‘unstructured’ which is where data is not organised in a structured format but machine learning can allow the data to be separated or organised, this might be data in emails or photos for example.
Data that is available in these forms exists in huge volumes and what data science allows is the processing and analysing of this data in order to identify patterns that can help with strategic decision making, increase business efficiency and optimise certain processes.
What is the history of data science in the real estate sector?
In terms of the real estate sector, in America the use of data science is at a point where it is very developed. Real estate companies are investing in property matching online software, which can determine if a property is a good match in terms of investment for each customer. Models that allow this have been developed by using a variety of public data and market information such as prices per square metre on past transactions, number of bedrooms and the quality of the neighbourhood. This provides clients with much wider parameters than could be provided by a single estate agent and allows the estate agent to offer greater accuracy in their information, for by example, giving clients accurate house prices instead of estimations. Zillow, an American real estate company, has developed a ‘Zestimate’ prediction, which predicts what the value of a property currently on sale will be anytime for one to ten years from now.
What makes data science in the real estate sector different to other sectors?
The real estate sector is a sector whose reach is vast, it is therefore affected by many social, political and economic factors which means that there is a huge amount of complex data available. It is through analysing and understanding this data that models can be created which aim to replicate the changes in the sector and evolve in order to anticipate what we might see in the future.
What role do you believe data science will play in the future of the real estate sector?
In the near future, data science will have an important role to play, as it will be able to not only improve a business strategy but also improve the way and quality of our lives. Data science which works in tandem with Artificial Intelligence (AI) will be able to analyse behaviour, interests and preferences in order to propose the ideal apartment for each client. This will mean that clients interested in a property will be able to visit it on their smartphone, projecting themselves into what it would be like to live there, by eliminating a wall or changing the colours for example.
Users installed within a building will also benefit from greater understanding of data. The IoT is becoming more and more necessary and used in AI within the real estate sector. Sensors which record temperature, air, equipment condition allow for responsive environments which adapt to user’s habits and behaviour. Data will thus have a direct impact on the chosen place of residence and investment criteria and will increasingly influence buying and selling habits.
Conclusions:
Data science can play a very important role in all the business sectors, each and every business will need a tailored model as the requirements will not be the same compared to other businesses in the same sector.
Wen it comes to construction and real estate unique strategical model can be developed to enhance business in the field. The clients fall in a variety of categories where the categories can be summarised according to the requirement of the clients and marketing can be focused more accurately with the help of Market Research, analysis and Prediction Modelling where the client expectation can be captured with an accuracy ranging from 75% - 97%.
References:
1. SAMIRA BOUADI, HEAD OF DATA SCIENCE, BNP PARIBAS REAL ESTATE.
2. Gabriel Morgan Asaftei, Sudeep Doshi, John Means, and Aditya Sanghvi
3. Nataliia Kharchenko, Big Data Made Simple
4. Ashwin D G, Data Scientist, Win Whispers.
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