Evidence map›Paper›PMID 38687809›Full record

ArticlePloS one2024

The financial risk of real estate combined factor analysis with grey prediction in Liaoning Province.

Jian-Lin Yuan, Nan Jing

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jian-Lin YuanSchool of Management, Liaoning University of Technology, Jinzhou, 121001, China.ORCID 0009-0003-9740-8722
Nan JingTangshan Letters and Calls Bureau of Hebei Province, Tangshan, 063000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The importance of real estate development has been widely accepted by all countries. Through early warning and avoidance of real estate financial risks, it can effectively promote the healthy and healthy development of the real estate industry, avoiding the impact of accidental factors, such as the COVID-19 pandemic, and promoting the overall economic development. Based on multiple regression analysis and grey prediction methods, this article constructs a real estate financial risk estimation model, and the real estate financial risk is estimated using the relevant data of Liaoning Province from 2001 to 2020. Analyzing the research results of financial risks in Liaoning Province, we can find that the real estate financial risks reached the peak in 2013, and then the real estate financial risks gradually showed a slow decline trend. In general, the financial risks in Liaoning Province are controllable. The study of financial risks in Liaoning Province will help to judge the development of the real estate industry and promote the continuous improvement of the overall economy. The article, through the study of real estate financial risks in Liaoning Province, can promote the development of regional real estate in Liaoning Province and promote the overall economic development of Liaoning Province, which has strong practical significance. The study of real estate financial risks, relevant risk research theories can be enriched, the identification of financial risks can be improved, and the study of real estate financial risks can be strengthened. The article uses a combination of multivariate statistics and grey fuzzy theory to complete the study of real estate financial risks. Therefore, through the exploration of multivariate statistics and grey fuzzy theory, its application value can be elevated.

Indexed as

COVID-19ChinaEconomic DevelopmentFactor Analysis, StatisticalHumansPandemicsSARS-CoV-2

Identifiers

PMID38687809
PMCPMC11060575

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.