SYSTEMS AND METHODS FOR FACILITATING GENERATION OF REAL ESTATE DESCRIPTIONS FOR REAL ESTATE ASSETS

Disclosed herein is a method for facilitating generation of real estate descriptions for real estate assets, in accordance with some embodiments. The method may include a step of receiving, using a communication device, real estate information of a real estate asset from a user device, a step of analyzing, using a processing device, the real estate information, a step of generating, using the processing device, a real estate description for the real estate asset using a machine learning model based on the analyzing, a step of receiving, using the communication device, a keyword relevant in a real estate industry from the user device, a step of generating, using the processing device, an optimized real estate description for the real estate asset based on the keyword and the real estate description, and storing, using a storage device, the optimized real estate description.

TECHNICAL FIELD

Generally, the present disclosure relates to the field of data processing. More specifically, the present disclosure relates to methods, systems, apparatuses, and devices for facilitating generation of real estate descriptions for real estate assets.

BACKGROUND

Generating descriptions for real estate requires human creativity. With the wide variety of properties available, it is difficult to craft fitting content that aptly describes a real estate listing. With thousands of brokers listing their properties, it is tough to compose or manually review the content uploaded by the user.

Existing techniques for facilitating generation of real estate descriptions for real estate assets are deficient with regard to several aspects. For instance, current technologies do not provide descriptions of the real estate assets that are search engine optimized. Furthermore, current technologies do not generate unique descriptions for real estate assets. Moreover, current technologies do not creatively generate descriptions for real estate assets.

Therefore, there is a need for methods, systems, apparatuses, and devices for facilitating generation of real estate descriptions for real estate assets that may overcome one or more of the above-mentioned problems and/or limitations.

BRIEF SUMMARY

Disclosed herein is a method for facilitating generation of real estate descriptions for real estate assets, in accordance with some embodiments. The method may include a step of receiving, using a communication device, at least one real estate information of at least one real estate asset from at least one user device. Further, the method may include a step of analyzing, using a processing device, the at least one real estate information. Further, the method may include a step of generating, using the processing device, at least one real estate description for the at least one real estate asset using at least one machine learning model based on the analyzing. Further, the method may include a step of receiving, using the communication device, at least one keyword relevant in a real estate industry from the at least one user device. Further, the method may include a step of generating, using the processing device, at least one optimized real estate description for the at least one real estate asset based on the at least one keyword and the at least one real estate description. Further, the method may include a step of storing, using a storage device, the at least one optimized real estate description.

Further disclosed herein is a method for facilitating generation of real estate descriptions for real estate assets, in accordance with some embodiments. The method may include a step of receiving, using a communication device, at least one real estate information of at least one real estate asset from at least one user device. Further, the method may include a step of receiving, using the communication device, at least one keyword relevant in a real estate industry from the at least one user device. Further, the method may include a step of analyzing, using a processing device, the at least one real estate information and the at least one keyword. Further, the method may include a step of generating, using the processing device, at least one optimized real estate description with respect to at least one search engine for the at least one real estate asset using at least one machine learning model based on the analyzing. Further, the method may include a step of storing, using a storage device, the at least one optimized real estate description.

Further disclosed herein is a system of facilitating generation of real estate descriptions for real estate assets, in accordance with some embodiments. The system may include a communication device, a processing device, and a storage device. Further, the communication device may be configured for performing a step of receiving at least one real estate information of at least one real estate asset from at least one user device. Further, the communication device may be configured for performing a step of receiving at least one keyword relevant in a real estate industry from the at least one user device. Further, the processing device may be communicatively coupled with the communication device. Further, the processing device may be configured for performing a step of analyzing the at least one real estate information. Further, the processing device may be configured for performing a step of generating at least one real estate description for the at least one real estate asset using at least one machine learning model based on the analyzing. Further, the processing device may be configured for performing a step of generating at least one optimized real estate description for the at least one real estate asset based on the at least one keyword and the at least one real estate description. Further, the storage device may be communicatively coupled with the processing device. Further, the storage device may be configured for performing a step of storing the at least one optimized real estate description.

DETAILED DESCRIPTION

The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of facilitating generation of real estate descriptions for real estate assets, embodiments of the present disclosure are not limited to use only in this context.

Definitions

The real estate descriptions may include marketable content on features and properties of the real estate assets.

The real estate assets may include a property. The property may be comprised of a land and a structure built on the land. The real estate asset may include a plot of land, a building, an apartment, etc.

The at least one real estate information may include one or more parameters or one or more characteristics of the at least one real estate asset. The one or more parameters may include a location, a builder, an area, a floor size, etc.

The at least one real estate description may include marketable content on features and properties of the at least one real estate asset.

The at least one real estate description may include at least one paragraph of the content that includes at least one key information associated with the at least one real estate asset in a well-stitched and smooth flowing manner.

The at least one machine learning model generates a textual description of the at least one real estate asset using the one or more parameters or the one or more characteristic of the at least one real estate asset. The at least one real estate description comprises the textual description.

The at least one optimized real estate asset may include the marketable content comprising the at least one keyword. Further, a ranking of the at least one optimized real estate description is higher than the ranking of the at least one real estate description with respect to a search engine.

The at least one search engine may include a Google™ search engine, a Yahoo™ search engine, a Bing™ search engine, etc.

The at least one search engine information may include one or more rules for ranking the content.

The one or more completion words may be used for forming unique and meaningful sentences and phrases with the one or more parameters.

The at least one completion word may include at least one semantic completion word.

The selecting of the at least one completion word further provides a semantic completion to the at least one real estate description.

The at least one keyword content may include descriptions, definitions, contexts, usages, synonyms, etc. of the at least one keyword.

The auto-regressive language model may include a GPT3 model.

The real estate descriptions may include marketable content on features and properties of the real estate assets.

The real estate assets may include a property. The property may be comprised of a land and a structure built on the land. The real estate asset may include a plot of land, a building, an apartment, etc.

Overview

The present disclosure describes methods, systems, apparatuses, and devices for facilitating generation of real estate descriptions for real estate assets.

Further, the present disclosure describes an automatic real estate description generator. Further, the real estate description generator generates the real estate description based on inputs from basic fields for taking SEO advantage. Further, the automatic real estate description generator is configured for:

1. Creating short description from 20+ parameters like location, builder, area info, floor size2. Getting SEO advantage for unique description that's created

Further, the present disclosure describes AI-based Real Estate Content Generation.

Further, the present disclosure describes a Real Estate content generation tool that generates highly marketable SEO-friendly unique content for properties. The model takes a few key parameters of the property as its input and then as output it generates a few paragraphs of content that contains all the key information provided in a well-stitched and smooth flowing manner. This AI-generated content as compared to human-generated content has resulted in much better results in terms of higher lift rate and click-through rates for those listings.

With the human creative talent being really costly and highly non-scaleable, we started analyzing a lot of real estate listings and the content. After analyzing about 5+ million listings and their content, we zeroed in a few key listing details that any user looks for while shortlisting a property. We were primarily catering to different sets of customers and based on these the content varied greatly.

1. Properties for Sale

2. Properties for Rent

The general pointers that played a key role in the user making a decision about the property were as follows:AreaLocalityCityAge of PropertyPrice of property

We weaved this key information from multiple listings and then trained a GPT3 model. We experimented with multiple variations and settings of the GPT3 model and finally settled down with the following setting.

Model Settings for Uniqueness: With the huge volume of the listings (10,000+) that flow into the listing platform daily, crafting unique content was next to impossible even for highly creative individuals who are plagued with cognitive biases. Each content will be customized and even when the tool is run for the same set of parameters, the model comes up with totally different and unique content. This has been achieved by using the higher value of temperature parameter which allows the models to come up with more creative content as the sampling is done from the completion words. Every content will be customized, and changes based on your parameters and settings provided. The text generated will also be SEO-friendly; each paragraph is generated in a way that will enhance search engine rankings by inserting keywords that we found were most relevant in the real estate industry. The model is a powerful content-generating tool that cuts weeks from content that was earlier composed by humans. All the content that is created is unique, SEO-friendly, and grammatically correct while being tailored to the needs and tastes of the real estate users.

Referring now to figures,FIG.1is an illustration of an online platform100consistent with various embodiments of the present disclosure. By way of non-limiting example, the online platform100to enable facilitating generation of real estate descriptions for real estate assets may be hosted on a centralized server102, such as, for example, a cloud computing service. The centralized server102may communicate with other network entities, such as, for example, a mobile device106(such as a smartphone, a laptop, a tablet computer, etc.), other electronic devices110(such as desktop computers, server computers, etc.), databases114, and sensors116over a communication network104, such as but not limited to, the Internet. Further, users of the online platform100may include relevant parties such as, but not limited to, end-users, administrators, service providers, service consumers, and so on. Accordingly, in some instances, electronic devices operated by the one or more relevant parties may be in communication with the platform.

A user112, such as the one or more relevant parties, may access online platform100through a web based software application or browser. The web based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device200.

With reference toFIG.2, a system consistent with an embodiment of the disclosure may include a computing device or cloud service, such as computing device200. In a basic configuration, computing device200may include at least one processing unit202and a system memory204. Depending on the configuration and type of computing device, system memory204may comprise, but is not limited to, volatile (e.g. random-access memory (RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory204may include operating system205, one or more programming modules206, and may include a program data207. Operating system205, for example, may be suitable for controlling computing device200's operation. In one embodiment, programming modules206may include image-processing module, machine learning module. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated inFIG.2by those components within a dashed line208.

Computing device200may have additional features or functionality. For example, computing device200may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated inFIG.2by a removable storage209and a non-removable storage210. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. System memory204, removable storage209, and non-removable storage210are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device200. Any such computer storage media may be part of device200. Computing device200may also have input device(s)212such as a keyboard, a mouse, a pen, a sound input device, a touch input device, a location sensor, a camera, a biometric sensor, etc. Output device(s)214such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

As stated above, a number of program modules and data files may be stored in system memory204, including operating system205. While executing on processing unit202, programming modules206(e.g., application220such as a media player) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above. The aforementioned process is an example, and processing unit202may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include machine learning applications.

FIG.3is a flow chart of a method300for facilitating generation of real estate descriptions for real estate assets, in accordance with some embodiments.

Further, at302, the method300may include receiving, using a communication device, at least one real estate information of at least one real estate asset from at least one user device.

Further, at304, the method300may include analyzing, using a processing device, the at least one real estate information.

Further, at306, the method300may include generating, using the processing device, at least one real estate description for the at least one real estate asset using at least one machine learning model based on the analyzing.

Further, at308, the method300may include receiving, using the communication device, at least one keyword relevant in a real estate industry from the at least one user device.

Further, at310, the method300may include generating, using the processing device, at least one optimized real estate description for the at least one real estate asset based on the at least one keyword and the at least one real estate description.

Further, at312, the method300may include storing, using a storage device, the at least one optimized real estate description.

In some embodiments, the storing of the at least one optimized real estate description may include storing the at least one optimized real estate description in a distributed ledger.

In some embodiments, the at least one real estate information may include one or more location information, builder information, size information, asset price information, amenities information, and asset age information of the at least one real estate asset.

In some embodiments, the at least one machine model may include at least one natural language generation model. Further, the at least one real estate description may include at least one natural language real estate description. Further, the generating of the at least one real estate description may include generating the at least one natural language real estate description using the at least one natural language generation model.

In some embodiments, the at least one machine learning model may be an autoregressive language model.

FIG.4is a flow chart of a method400for facilitating generation of real estate descriptions for real estate assets, in accordance with further embodiments.FIG.5is a continuation flow chart ofFIG.4. Further, at402, the method400may include optimizing, using the processing device, the at least one real estate description based on the at least one keyword. Further, the optimizing comprises performing at least one optimizing operation on the at least one real estate description based on the at least one keyword. Further, the at least one optimizing operation comprises at least one of editing, inserting, and formatting. Further, the generating of the at least one optimized real estate description is based on the optimizing.

FIG.6is a flow chart of a method600for facilitating generation of real estate descriptions for real estate assets, in accordance with further embodiments.FIG.7is a continuation flow chart ofFIG.6. Further, the at least one real estate description may be associated with a ranking with respect to at least one search engine. Further, at602, the method600may include retrieving, using the storage device, at least one search engine information associated with the at least one search engine. Further, the optimizing of the at least real estate description using the at least one keyword is based on the at least one search engine information. Further, the optimizing of the at least one real estate asset improves the ranking of the at least one real estate description.

FIG.8is a flow chart of a method800for facilitating generation of real estate descriptions for real estate assets, in accordance with some embodiments. Further, at802, the method800may include retrieving, using the storage device, one or more completion words for the generation of the real estate descriptions. Further, at804, the method800may include selecting, using the processing device, at least one completion word from the one or more completion words based on the analyzing of the at least one real estate information. Further, the selecting of the at least one completion word provides a uniqueness to the at least one real estate description. Further, the generating of the at least one real estate description is based on the selecting of the at least one completion word.

FIG.9is a flow chart of a method900for facilitating generation of real estate descriptions for real estate assets, in accordance with some embodiments. Further, at902, the method900may include analyzing, using the processing device, the at least one keyword. Further, at904, the method900may include retrieving, using the storage device, at least one keyword content associated with the at least one keyword based on the analyzing of the at least one keyword. Further, the generating of the at least one optimized real estate description is based on the at least one keyword content.

FIG.10is a flow chart of a method1000for facilitating generation of real estate descriptions for real estate assets, in accordance with some embodiments.

Further, at1002, the method1000may include receiving, using a communication device, at least one real estate information of at least one real estate asset from at least one user device.

Further, at1004, the method1000may include receiving, using the communication device, at least one keyword relevant in a real estate industry from the at least one user device.

Further, at1006, the method1000may include analyzing, using a processing device, the at least one real estate information and the at least one keyword.

Further, at1008, the method1000may include generating, using the processing device, at least one optimized real estate description with respect to at least one search engine for the at least one real estate asset using at least one machine learning model based on the analyzing.

Further, at1010, the method1000may include storing, using a storage device, the at least one optimized real estate description.

In some embodiments, the storing of the at least one optimized real estate description may include storing the at least one optimized real estate description in a distributed ledger.

FIG.11is a block diagram of a system1100for facilitating generation of real estate descriptions for real estate assets, in accordance with some embodiments. The system1100may include a communication device1102, a processing device1104, and a storage device1106.

Further, the communication device1102may be configured for performing a step of receiving at least one real estate information of at least one real estate asset from at least one user device.

Further, the communication device1102may be configured for performing a step of receiving at least one keyword relevant in a real estate industry from the at least one user device.

Further, the processing device1104may be communicatively coupled with the communication device1102.

Further, the processing device1104may be configured for performing a step of analyzing the at least one real estate information.

Further, the processing device1104may be configured for performing a step of generating at least one real estate description for the at least one real estate asset using at least one machine learning model based on the analyzing.

Further, the processing device1104may be configured for performing a step of generating at least one optimized real estate description for the at least one real estate asset based on the at least one keyword and the at least one real estate description.

Further, the storage device1106may be communicatively coupled with the processing device1104.

Further, the storage device1106may be configured for performing a step of storing the at least one optimized real estate description.

In some embodiments, the storing of the at least one optimized real estate description may include storing the at least one optimized real estate description in a distributed ledger.

In some embodiments, the at least one real estate information may include one or more of location information, builder information, size information, asset price information, amenities information, and asset age information of the at least one real estate asset.

In some embodiments, the at least one machine model may include at least one natural language generation model. Further, the at least one real estate description may include at least one natural language real estate description. Further, the generating of the at least one real estate description may include generating the at least one natural language real estate description using the at least one natural language generation model.

In some embodiments, the at least one machine learning model may be an autoregressive language model.

In some embodiments, the processing device1104may be configured for performing a step of optimizing the at least one real estate description based on the at least one keyword. Further, the optimizing may include performing at least one optimizing operation on the at least one real estate description based on the at least one keyword. Further, the at least one optimizing operation may include one or more of editing, inserting, and formatting. Further, the generating of the at least one optimized real estate description may be based on the optimizing.

In some embodiments, the at least one real estate description may be associated with a ranking with respect to at least one search engine. Further, the storage device1106may be configured for performing a step of retrieving at least one search engine information associated with the at least one search engine. Further, the optimizing of the at least real estate description using the at least one keyword may be based on the at least one search engine information. Further, the optimizing of the at least one real estate asset improves the ranking of the at least one real estate description.

In some embodiments, the storage device1106may be configured for performing a step of retrieving one or more completion words for the generation of the real estate descriptions. Further, the processing device1104may be configured for performing a step of selecting at least one completion word from the one or more completion words based on the analyzing of the at least one real estate information. Further, the selecting of the at least one completion word provides a uniqueness to the at least one real estate description. Further, the generating of the at least one real estate description may be based on the selecting of the at least one completion word.

In some embodiments, the processing device1104may be configured for performing a step of analyzing the at least one keyword. Further, the storage device1106may be configured for performing a step of retrieving at least one keyword content associated with the at least one keyword based on the analyzing of the at least one keyword. Further, the generating of the at least one optimized real estate description may be based on the at least one keyword content.

FIG.12is a schematic diagram of an area map1200of an area associated with a real estate asset, in accordance with some embodiments. Further, the area map1200may include legal information associated with the real estate asset, civic infrastructure information of civic infrastructures present in the area, future planned development information of future planned developments in the area, price information associated with prices of the real estate, construction information associated with constructions in the real estate asset, project history information associated with a project history of the real estate asset, builder profile information associated with a builder profile of a builder associated with the real estate asset, and watch outs information associated with watch outs of the area. Further, the legal information may include a land acquisition status, license chronology, environment clearances, etc. Further, the civic infrastructures information roads and connectivity, sewage and pipelines, electricity and utilities, etc. Further, the future planned development information may include master plan information, metro and other connectivity, business hubs, malls and shopping, etc. Further, the price information may include real time market price, builder prices, special schemes, etc. Further, the construction information may include tower wise construction status, place of construction, quality of construction, etc. Further, the project history information may include launch data, major events and price triggers, major flags +ve and −ve, etc. Further, the builder profile information past projects delivery, in pipeline projects, creditworthiness and financial strength, etc. Further, the watch outs information may include adjoining villages, slum, drains, STPs, under construction zones, etc.